survey

3D Brain and Heart Volume Generative Models: A Survey

Authors:
Yanbin Liu

Harry Perkins Institute of Medical Research, Department of Computer Science and Software Engineering, The University of Western Australia, Australia

Harry Perkins Institute of Medical Research, Department of Computer Science and Software Engineering, The University of Western Australia, Australia

0000-0003-4724-8065
Search about this author

,
Girish Dwivedi

Harry Perkins Institute of Medical Research, The University of Western Australia, Fiona Stanley Hospital, Australia

Harry Perkins Institute of Medical Research, The University of Western Australia, Fiona Stanley Hospital, Australia

0000-0003-0717-740X
Search about this author

,
Farid Boussaid

Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia

Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia

0000-0001-7250-7407
Search about this author

,
Mohammed Bennamoun

Department of Computer Science and Software Engineering, The University of Western Australia, Australia

Department of Computer Science and Software Engineering, The University of Western Australia, Australia

0000-0002-6603-3257
Search about this author

Authors Info & Claims

ACM Computing Surveys Volume 56 Issue 6Article No.: 137pp 1–37https://doi.org/10.1145/3638044

Published:22 January 2024Publication History

ACM Computing Surveys

Abstract

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This article provides a comprehensive survey of generative models for 3D volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task, and also suggest potential future directions. A list of the latest publications will be updated on GitHub to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey.

REFERENCES

[1] Ahmed Aya Saleh, El-Behaidy Wessam H., and Youssif Aliaa A. A.. 2021. Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction. Biomedical Signal Processing and Control 69 (2021), 102842.Google Scholar
[2] AIMI Stanford. 2022. COCA—Coronary Calcium and Chest CT’s Dataset. Retrieved December 22, 2023 from https://stanfordaimi.azurewebsites.net/datasets/e8ca74dc-8dd4-4340-815a-60b41f6cb2aaGoogle Scholar
[3] Akkus Zeynettin, Galimzianova Alfiia, Hoogi Assaf, Rubin Daniel L., and Erickson Bradley J.. 2017. Deep learning for brain MRI segmentation: State of the art and future directions. Journal of Digital Imaging 30, 4 (2017), 449–459.Google ScholarCross Ref
[4] AlAmir Manal and AlGhamdi Manal. 2022. The role of generative adversarial network in medical image analysis: An in-depth survey. ACM Computing Surveys 55, 5 (2022), Article 96, 36 pages.Google Scholar
[5] Ali Hazrat, Biswas Rafiul, Ali Farida, Shah Uzair, Alamgir Asma, Mousa Osama, and Shah Zubair. 2022. The role of generative adversarial networks in brain MRI: A scoping review. Insights into Imaging 13, 1 (2022), 1–15.Google Scholar
[6] Amirrajab Sina, Abbasi-Sureshjani Samaneh, Khalil Yasmina Al, Lorenz Cristian, Weese Juergen, Pluim Josien, and Breeuwer Marcel. 2020. XCAT-GAN for synthesizing 3D consistent labeled cardiac MR images on anatomically variable XCAT phantoms. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 128–137.Google ScholarDigital Library
[7] Andreopoulos Alexander and Tsotsos John K.. 2008. Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Medical Image Analysis 12, 3 (2008), 335–357.Google ScholarCross Ref
[8] Arjovsky Martin, Chintala Soumith, and Bottou Léon. 2017. Wasserstein generative adversarial networks. In Proceedings of the International Conference on Machine Learning.214–223.Google Scholar
[9] Bakas Spyridon, Akbari Hamed, Sotiras Aristeidis, Bilello Michel, Rozycki Martin, Kirby Justin S., Freymann John B., Farahani Keyvan, and Davatzikos Christos. 2017. Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data 4, 1 (2017), 1–13.Google ScholarCross Ref
[10] Balakrishnan Guha, Zhao Amy, Sabuncu Mert R., Guttag John, and Dalca Adrian V.. 2018. An unsupervised learning model for deformable medical image registration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9252–9260.Google ScholarCross Ref
[11] Balakrishnan Guha, Zhao Amy, Sabuncu Mert R., Guttag John, and Dalca Adrian V.. 2019. VoxelMorph: A learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging 38, 8 (2019), 1788–1800.Google ScholarCross Ref
[12] Basaran Berke Doga, Qiao Mengyun, Matthews Paul M., and Bai Wenjia. 2022. Subject-specific lesion generation and pseudo-healthy synthesis for multiple sclerosis brain images. In Proceedings of the International Workshop on Simulation and Synthesis in Medical Imaging. 1–11.Google ScholarDigital Library
[13] Bernard Olivier, Lalande Alain, Zotti Clement, Cervenansky Frederick, Yang Xin, Heng Pheng-Ann, Cetin Irem, Lekadir Karim, Camara Oscar, Ballester Miguel Angel Gonzalez, et al. 2018. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Transactions on Medical Imaging 37, 11 (2018), 2514–2525.Google ScholarCross Ref
[14] Biffi Carlo, Oktay Ozan, Tarroni Giacomo, Bai Wenjia, Marvao Antonio De, Doumou Georgia, Rajchl Martin, Bedair Reem, Prasad Sanjay, Cook Stuart, et al. 2018. Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 464–471.Google ScholarDigital Library
[15] Borji Ali. 2019. Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding 179 (2019), 41–65.Google ScholarDigital Library
[16] Bourlard Hervé and Kamp Yves. 1988. Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics 59, 4 (1988), 291–294.Google ScholarDigital Library
[17] Bustamante Mariana, Viola Federica, Engvall Jan, Carlhäll Carl-Johan, and Ebbers Tino. 2023. Automatic time-resolved cardiovascular segmentation of 4D flow MRI using deep learning. Journal of Magnetic Resonance Imaging 57, 1 (2023), 191–203.Google ScholarCross Ref
[18] Chen Sihong, Ma Kai, and Zheng Yefeng. 2019. Med3D: Transfer learning for 3D medical image analysis. arXiv preprint arXiv:1904.00625 (2019).Google Scholar
[19] Chen Wei, Liu Boqiang, Peng Suting, Sun Jiawei, and Qiao Xu. 2018. S3D-UNet: Separable 3D U-net for brain tumor segmentation. In Proceedings of the International MICCAI Brainlesion Workshop. 358–368.Google Scholar
[20] Chen Yicheng, Jakary Angela, Avadiappan Sivakami, Hess Christopher P., and Lupo Janine M.. 2020. QSMGAN: Improved quantitative susceptibility mapping using 3D generative adversarial networks with increased receptive field. NeuroImage 207 (2020), 116389.Google ScholarCross Ref
[21] Chen Yizhou, Yang Xu-Hua, Wei Zihan, Heidari Ali Asghar, Zheng Nenggan, Li Zhicheng, Chen Huiling, Hu Haigen, Zhou Qianwei, and Guan Qiu. 2022. Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144 (2022), 105382.Google Scholar
[22] Cho Kyunghyun, Merrienboer B. van, Gulcehre Caglar, Bougares F., Schwenk H., and Bengio Yoshua. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP ’14).Google ScholarCross Ref
[23] Choi Yunjey, Choi Minje, Kim Munyoung, Ha Jung-Woo, Kim Sunghun, and Choo Jaegul. 2018. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8789–8797.Google ScholarCross Ref
[24] Chong Chee Keong and Ho Eric Tatt Wei. 2021. Synthesis of 3D MRI brain images with shape and texture generative adversarial deep neural networks. IEEE Access 9 (2021), 64747–64760.Google ScholarCross Ref
[25] Christensen Gary E., Geng Xiujuan, Kuhl Jon G., Bruss Joel, Grabowski Thomas J., Pirwani Imran A., Vannier Michael W., Allen John S., and Damasio Hanna. 2006. Introduction to the Non-rigid Image Registration Evaluation Project (NIREP). In Proceedings of the International Workshop on Biomedical Image Registration. 128–135.Google ScholarDigital Library
[26] Chung Hyungjin, Cha Eunju, Sunwoo Leonard, and Ye Jong Chul. 2021. Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data. Medical Image Analysis 71 (2021), 102047.Google ScholarCross Ref
[27] Çiçek Özgün, Abdulkadir Ahmed, Lienkamp Soeren S., Brox Thomas, and Ronneberger Olaf. 2016. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 424–432.Google Scholar
[28] Cirillo Marco Domenico, Abramian David, and Eklund Anders. 2020. Vox2Vox: 3D-GAN for brain tumour segmentation. In Proceedings of the International MICCAI Brainlesion Workshop. 274–284.Google Scholar
[29] Cocosco Chris A., Kollokian Vasken, Kwan Remi K.-S., Pike G. Bruce, and Evans Alan C.. 1997. BrainWeb: Online interface to a 3D MRI simulated brain database. NeuroImage 5, 4 (1997), 425.Google Scholar
[30] Collobert Ronan, Weston Jason, Bottou Léon, Karlen Michael, Kavukcuoglu Koray, and Kuksa Pavel. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research 12 (2011), 2493–2537.Google ScholarDigital Library
[31] Commowick Olivier, Cervenansky Frédéric, and Ameli Roxana. 2016. MSSEG challenge proceedings: Multiple sclerosis lesions segmentation challenge using a data management and processing infrastructure. In Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI ’16). 126.Google Scholar
[32] Consortium ADHD-200. 2012. The ADHD-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience 6 (2012), 62.Google ScholarCross Ref
[33] Dagley Alexander, LaPoint Molly, Huijbers Willem, Hedden Trey, McLaren Donald G., Chatwal Jasmeer P., Papp Kathryn V., Amariglio Rebecca E., Blacker Deborah, Rentz Dorene M., et al. 2017. Harvard aging brain study: Dataset and accessibility. NeuroImage 144 (2017), 255–258.Google ScholarCross Ref
[34] Dalmaz Onat, Yurt Mahmut, and Çukur Tolga. 2021. ResViT: Residual vision transformers for multi-modal medical image synthesis. arXiv preprint arXiv:2106.16031 (2021).Google Scholar
[35] Dar Salman U. H., Yurt Mahmut, Karacan Levent, Erdem Aykut, Erdem Erkut, and Cukur Tolga. 2019. Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Transactions on Medical Imaging 38, 10 (2019), 2375–2388.Google ScholarCross Ref
[36] Dataset IXI. 2010. IXI Brain Development Home Page. Retrieved December 22, 2023 from https://brain-development.org/ixi-dataset/Google Scholar
[37] Harder Annemarie M. den, Wolterink Jelmer M., Willemink Martin J., Schilham Arnold M. R., Jong Pim A. de, Budde Ricardo P. J., Nathoe Hendrik M., Išgum Ivana, and Leiner Tim. 2016. Submillisievert coronary calcium quantification using model-based iterative reconstruction: A within-patient analysis. European Journal of Radiology 85, 11 (2016), 2152–2159.Google ScholarCross Ref
[38] Deng Jia, Dong Wei, Socher Richard, Li Li-Jia, Li Kai, and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA, 248–255.Google ScholarCross Ref
[39] Dhar Tribikram, Dey Nilanjan, Borra Surekha, and Sherratt R. Simon. 2023. Challenges of deep learning in medical image analysis—Improving explainability and trust. IEEE Transactions on Technology and Society 4, 1 (2023), 68–75.Google ScholarCross Ref
[40] Dhariwal Prafulla and Nichol Alexander. 2021. Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems 34 (2021), 8780–8794.Google Scholar
[41] Martino Adriana Di, Yan Chao-Gan, Li Qingyang, Denio Erin, Castellanos Francisco X., Alaerts Kaat, Anderson Jeffrey S., Assaf Michal, Bookheimer Susan Y., Dapretto Mirella, et al. 2014. The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry 19, 6 (2014), 659–667.Google ScholarCross Ref
[42] Dong Suyu, Luo Gongning, Wang Kuanquan, Cao Shaodong, Mercado Ashley, Shmuilovich Olga, Zhang Henggui, and Li Shuo. 2018. VoxelAtlasGAN: 3D left ventricle segmentation on echocardiography with atlas guided generation and voxel-to-voxel discrimination. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 622–629.Google ScholarDigital Library
[43] El-Rewaidy Hossam, Neisius Ulf, Mancio Jennifer, Kucukseymen Selcuk, Rodriguez Jennifer, Paskavitz Amanda, Menze Bjoern, and Nezafat Reza. 2020. Deep complex convolutional network for fast reconstruction of 3D late gadolinium enhancement cardiac MRI. NMR in Biomedicine 33, 7 (2020), e4312.Google ScholarCross Ref
[44] Elazab Ahmed, Wang Changmiao, Gardezi Syed Jamal Safdar, Bai Hongmin, Hu Qingmao, Wang Tianfu, Chang Chunqi, and Lei Baiying. 2020. GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images. Neural Networks 132 (2020), 321–332.Google ScholarCross Ref
[45] Elsken Thomas, Metzen Jan Hendrik, and Hutter Frank. 2019. Neural architecture search: A survey. Journal of Machine Learning Research 20, 1 (2019), 1997–2017.Google ScholarDigital Library
[46] Enokiya Yuki, Iwamoto Yutaro, Chen Yen-Wei, and Han Xian-Hua. 2018. Automatic liver segmentation using U-Net with Wasserstein GANs. Journal of Image and Graphics 6, 2 (2018), 152–159.Google ScholarCross Ref
[47] Esser Patrick, Rombach Robin, and Ommer Bjorn. 2021. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12873–12883.Google ScholarCross Ref
[48] Farnia Farzan and Ozdaglar Asuman. 2020. Do GANs always have Nash equilibria? In Proceedings of the International Conference on Machine Learning. 3029–3039.Google Scholar
[49] Fischl Bruce. 2012. FreeSurfer. NeuroImage 62, 2 (2012), 774–781.Google ScholarCross Ref
[50] Gal Yarin and Ghahramani Zoubin. 2016. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the International Conference on Machine Learning. 1050–1059.Google ScholarDigital Library
[51] Gao Wenshuo, Zhang Xiaoguang, Yang Lei, and Liu Huizhong. 2010. An improved Sobel edge detection. In Proceedings of the 2010 3rd International Conference on Computer Science and Information Technology, Vol. 5. IEEE, Los Alamitos, CA, 67–71.Google Scholar
[52] Girshick Ross. 2015. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 1440–1448.Google ScholarDigital Library
[53] Gollub Randy L., Shoemaker Jody M., King Margaret D., White Tonya, Ehrlich Stefan, Sponheim Scott R., Clark Vincent P., Turner Jessica A., Mueller Bryon A., Magnotta Vince, et al. 2013. The MCIC collection: A shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics 11, 3 (2013), 367–388.Google ScholarCross Ref
[54] Gondara Lovedeep. 2016. Medical image denoising using convolutional denoising autoencoders. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW ’16). IEEE, Los Alamitos, CA, 241–246.Google ScholarCross Ref
[55] Goodfellow Ian, Pouget-Abadie Jean, Mirza Mehdi, Xu Bing, Warde-Farley David, Ozair Sherjil, Courville Aaron, and Bengio Yoshua. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems 27 (2014), 1–9.Google Scholar
[56] Granstedt Jason L., Kelkar Varun A., Zhou Weimin, and Anastasio Mark A.. 2021. SlabGAN: A method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. In Medical Imaging 2021: Image Processing, Vol. 11596. SPIE, 329–335.Google Scholar
[57] Graves Alex, Mohamed Abdel-Rahman, and Hinton Geoffrey. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, Los Alamitos, CA, 6645–6649.Google ScholarCross Ref
[58] Gregor Karol, Danihelka Ivo, Mnih Andriy, Blundell Charles, and Wierstra Daan. 2014. Deep autoregressive networks. In Proceedings of the International Conference on Machine Learning. 1242–1250.Google Scholar
[59] Gulrajani Ishaan, Ahmed Faruk, Arjovsky Martin, Dumoulin Vincent, and Courville Aaron C.. 2017. Improved training of Wasserstein GANs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS ’17). 5769–5779.Google Scholar
[60] Habijan Marija, Galić Irena, Leventić Hrvoje, and Romić Krešimir. 2021. Whole heart segmentation using 3D FM-pre-ResNet encoder–decoder based architecture with variational autoencoder regularization. Applied Sciences 11, 9 (2021), 3912.Google ScholarCross Ref
[61] Han Changhee, Rundo Leonardo, Murao Kohei, Noguchi Tomoyuki, Shimahara Yuki, Milacski Zoltán Ádám, Koshino Saori, Sala Evis, Nakayama Hideki, and Satoh Shin’ichi. 2021. MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinformatics 22, 2 (2021), 1–20.Google Scholar
[62] He Kaiming, Chen Xinlei, Xie Saining, Li Yanghao, Dollár Piotr, and Girshick Ross. 2022. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16000–16009.Google ScholarCross Ref
[63] He Kaiming, Gkioxari Georgia, Dollár Piotr, and Girshick Ross. 2017. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 2961–2969.Google ScholarCross Ref
[64] He Kaiming, Zhang Xiangyu, Ren Shaoqing, and Sun Jian. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.Google ScholarCross Ref
[65] Hernandez Monica. 2014. Gauss–Newton inspired preconditioned optimization in large deformation diffeomorphic metric mapping. Physics in Medicine & Biology 59, 20 (2014), 6085.Google ScholarCross Ref
[66] Heusel Martin, Ramsauer Hubert, Unterthiner Thomas, Nessler Bernhard, and Hochreiter Sepp. 2017. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS ’17). 6629–6640.Google Scholar
[67] Zemel Geoffrey E. Hinton and Richard S.. 1993. Autoencoders, minimum description length, and Helmholtz free energy. In Proceedings of the 6th International Conference on Neural Information Processing Systems (NIPS ’93). 3–10.Google Scholar
[68] Hinton Geoffrey, Deng Li, Yu Dong, Dahl George E., Mohamed Abdel-Rahman, Jaitly Navdeep, Senior Andrew, Vanhoucke Vincent, Nguyen Patrick, Sainath Tara N., et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29, 6 (2012), 82–97.Google ScholarCross Ref
[69] Hinz Tobias, Fisher Matthew, Wang Oliver, and Wermter Stefan. 2021. Improved techniques for training single-image GANs. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1300–1309.Google ScholarCross Ref
[70] Ho Jonathan, Jain Ajay, and Abbeel Pieter. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840–6851.Google Scholar
[71] Ho Jonathan, Saharia Chitwan, Chan William, Fleet David J., Norouzi Mohammad, and Salimans Tim. 2022. Cascaded diffusion models for high fidelity image generation. Journal of Machine Learning Research 23 (2022), 47–51.Google Scholar
[72] Hodge S., Haselgrove C., Kennedy D., and Frazier J.. 2009. CANDIShare: A resource for pediatric neuroimaging data. Neuroinformatics 10, 3 (2009), 319–322.Google Scholar
[73] Holmes Avram J., Hollinshead Marisa O., O’Keefe Timothy M., Petrov Victor I., Fariello Gabriele R., Wald Lawrence L., Fischl Bruce, Rosen Bruce R., Mair Ross W., Roffman Joshua L., et al. 2015. Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Scientific Data 2, 1 (2015), 1–16.Google ScholarCross Ref
[74] Hong Sungmin, Marinescu Razvan, Dalca Adrian V., Bonkhoff Anna K., Bretzner Martin, Rost Natalia S., and Golland Polina. 2021. 3D-StyleGAN: A style-based generative adversarial network for generative modeling of three-dimensional medical images. In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. Springer, 24–34.Google ScholarDigital Library
[75] Hongtao Zhang, Shinomiya Yuki, and Yoshida Shinichi. 2020. 3D brain MRI reconstruction based on 2D super-resolution technology. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC ’20). IEEE, Los Alamitos, CA, 18–23.Google ScholarDigital Library
[76] Hotter Benjamin, Pittl Sandra, Ebinger Martin, Oepen Gabriele, Jegzentis Kati, Kudo Kohsuke, Rozanski Michal, Schmidt Wolf U., Brunecker Peter, Xu Chao, et al. 2009. Prospective study on the mismatch concept in acute stroke patients within the first 24 h after symptom onset—1000Plus study. BMC Neurology 9, 1 (2009), 1–8.Google ScholarCross Ref
[77] Hou Yutai, Lai Yongkui, Wu Yushan, Che Wanxiang, and Liu Ting. 2021. Few-shot learning for multi-label intent detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 13036–13044.Google ScholarCross Ref
[78] Huang Yawen, Zheng Feng, Cong Runmin, Huang Weilin, Scott Matthew R., and Shao Ling. 2020. MCMT-GAN: Multi-task coherent modality transferable GAN for 3D brain image synthesis. IEEE Transactions on Image Processing 29 (2020), 8187–8198.Google ScholarCross Ref
[79] Iqbal Ahmed, Sharif Muhammad, Yasmin Mussarat, Raza Mudassar, and Aftab Shabib. 2022. Generative adversarial networks and its applications in the biomedical image segmentation: A comprehensive survey. International Journal of Multimedia Information Retrieval 11 (2022), 333–368.Google ScholarCross Ref
[80] iSeg-2017. 2022. 6-Month Infant MRI Brain Segmentation. Retrieved December 22, 2023 from https://iseg2017.web.unc.edu/reference/Google Scholar
[81] Isola Phillip, Zhu Jun-Yan, Zhou Tinghui, and Efros Alexei A.. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1125–1134.Google ScholarCross Ref
[82] Jaderberg Max, Simonyan Karen, Zisserman Andrew, and Koray Kavukcuoglu. 2015. Spatial transformer networks. Advances in Neural Information Processing Systems 28 (2015), 1–9.Google Scholar
[83] Jeong Jiwoong J., Tariq Amara, Adejumo Tobiloba, Trivedi Hari, Gichoya Judy W., and Banerjee Imon. 2022. Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation. Journal of Digital Imaging35, 2 (2022), 137–152.Google ScholarCross Ref
[84] Ji Shuiwang, Xu Wei, Yang Ming, and Yu Kai. 2012. 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2012), 221–231.Google ScholarDigital Library
[85] Joyce Thomas and Kozerke Sebastian. 2019. 3D medical image synthesis by factorised representation and deformable model learning. In Proceedings of the International Workshop on Simulation and Synthesis in Medical Imaging. 110–119.Google ScholarDigital Library
[86] Karras Tero, Aila Timo, Laine Samuli, and Lehtinen Jaakko. 2017. Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017).Google Scholar
[87] Karras Tero, Laine Samuli, and Aila Timo. 2019. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4401–4410.Google ScholarCross Ref
[88] Karras Tero, Laine Samuli, Aittala Miika, Hellsten Janne, Lehtinen Jaakko, and Aila Timo. 2020. Analyzing and improving the image quality of StyleGAN. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8110–8119.Google ScholarCross Ref
[89] Kazeminia Salome, Baur Christoph, Kuijper Arjan, Ginneken Bram van, Navab Nassir, Albarqouni Shadi, and Mukhopadhyay Anirban. 2020. GANs for medical image analysis. Artificial Intelligence in Medicine 109 (2020), 101938.Google ScholarCross Ref
[90] Kenton Jacob Devlin Ming-Wei Chang and Toutanova Lee Kristina. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT. 4171–4186.Google Scholar
[91] Kim Boah and Ye Jong Chul. 2022. Diffusion deformable model for 4D temporal medical image generation. arXiv preprint arXiv:2206.13295 (2022).Google Scholar
[92] Kingma Diederik P. and Welling Max. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).Google Scholar
[93] Klein Arno, Ghosh Satrajit S., Bao Forrest S., Giard Joachim, Häme Yrjö, Stavsky Eliezer, Lee Noah, Rossa Brian, Reuter Martin, Neto Elias Chaibub, et al. 2017. Mindboggling morphometry of human brains. PLoS Computational Biology 13, 2 (2017), e1005350.Google ScholarCross Ref
[94] Kolarik Martin, Burget Radim, Travieso-Gonzalez Carlos M., and Kocica Jan. 2021. Planar 3D transfer learning for end to end unimodal MRI unbalanced data segmentation. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR ’21). IEEE, Los Alamitos, CA, 6051–6058.Google ScholarCross Ref
[95] Kopuklu Okan, Kose Neslihan, Gunduz Ahmet, and Rigoll Gerhard. 2019. Resource efficient 3D convolutional neural networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops.Google ScholarCross Ref
[96] Krebs Julian, Delingette Hervé, Ayache Nicholas, and Mansi Tommaso. 2021. Learning a generative motion model from image sequences based on a latent motion matrix. IEEE Transactions on Medical Imaging 40, 5 (2021), 1405–1416.Google ScholarCross Ref
[97] Krizhevsky Alex, Sutskever Ilya, and Hinton Geoffrey E.. 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (2012), 1–9.Google Scholar
[98] Kruthika K. R., Maheshappa H. D., and Alzheimer’s Disease Neuroimaging Initiative. 2019. CBIR system using Capsule Networks and 3D CNN for Alzheimer’s disease diagnosis. Informatics in Medicine Unlocked 14 (2019), 59–68.Google ScholarCross Ref
[99] Kuijf Hugo J., Biesbroek J. Matthijs, Bresser Jeroen De, Heinen Rutger, Andermatt Simon, Bento Mariana, Berseth Matt, Belyaev Mikhail, Cardoso M. Jorge, Casamitjana Adria, et al. 2019. Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge. IEEE Transactions on Medical Imaging 38, 11 (2019), 2556–2568.Google ScholarCross Ref
[100] Kwon Gihyun, Han Chihye, and Kim Dae-Shik. 2019. Generation of 3D brain MRI using auto-encoding generative adversarial networks. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 118–126.Google ScholarDigital Library
[101] LaMontagne Pamela J., Benzinger Tammie L. S., Morris John C., Keefe Sarah, Hornbeck Russ, Xiong Chengjie, Grant Elizabeth, Hassenstab Jason, Moulder Krista, Vlassenko Andrei G., et al. 2019. OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv 2019 (2019), 1–37.Google Scholar
[102] Lan Haoyu, Toga Arthur W., Sepehrband Farshid, and Alzheimer’s Disease Neuroimaging Initiative. 2020. SC-GAN: 3D self-attention conditional GAN with spectral normalization for multi-modal neuroimaging synthesis. bioRxiv 2020 (2020), 1–35.Google Scholar
[103] Larochelle Hugo and Murray Iain. 2011. The neural autoregressive distribution estimator. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 29–37.Google Scholar
[104] LeCun Yann. 1987. Modeles connexionnistes de l’apprentissage (Connectionist Learning Models). Ph.D. Dissertation. Laboratoire de Dynamique des Reseaux.Google Scholar
[105] LeCun Yann, Bottou Léon, Bengio Yoshua, and Haffner Patrick. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (1998), 2278–2324.Google ScholarCross Ref
[106] Lee Jihyun, Sung Minhyuk, Kim Hyunjin, and Kim Tae-Kyun. 2022. Pop-out motion: 3D-aware image deformation via learning the shape Laplacian. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18532–18541.Google ScholarCross Ref
[107] Lell Michael M. and Kachelrieß Marc. 2020. Recent and upcoming technological developments in computed tomography: High speed, low dose, deep learning, multienergy. Investigative Radiology 55, 1 (2020), 8–19.Google ScholarCross Ref
[108] Lesjak Žiga, Galimzianova Alfiia, Koren Aleš, Lukin Matej, Pernuš Franjo, Likar Boštjan, and Špiclin Žiga. 2018. A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus. Neuroinformatics 16, 1 (2018), 51–63.Google ScholarCross Ref
[109] Li Chuan and Wand Michael. 2016. Precomputed real-time texture synthesis with Markovian generative adversarial networks. In Proceedings of the European Conference on Computer Vision. 702–716.Google ScholarCross Ref
[110] Li Shuailin, Zhang Chuyu, and He Xuming. 2020. Shape-aware semi-supervised 3D semantic segmentation for medical images. In Proceedings of the International Conference on Medical Image Computing, and Computer-Assisted Intervention. 552–561.Google ScholarDigital Library
[111] Liao Haofu, Tang Yucheng, Funka-Lea Gareth, Luo Jiebo, and Zhou Shaohua Kevin. 2018. More knowledge is better: Cross-modality volume completion and 3D+2D segmentation for intracardiac echocardiography contouring. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 535–543.Google ScholarDigital Library
[112] Liew Sook-Lei, Anglin Julia M., Banks Nick W., Sondag Matt, Ito Kaori L., Kim Hosung, Chan Jennifer, Ito Joyce, Jung Connie, Khoshab Nima, et al. 2018. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific Data 5, 1 (2018), 1–11.Google ScholarCross Ref
[113] Lin Wanyun. 2020. Synthesizing missing data using 3D reversible GAN for Alzheimer’s disease. In Proceedings of the 2020 International Symposium on Artificial Intelligence in Medical Sciences. 208–213.Google ScholarDigital Library
[114] Lin Wanyun, Lin Weiming, Chen Gang, Zhang Hejun, Gao Qinquan, Huang Yechong, Tong Tong, Du Min, and Initiative Alzheimer’s Disease Neuroimaging. 2021. Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer’s disease. Frontiers in Neuroscience 15 (2021), 646013.Google ScholarCross Ref
[115] Liu Jing, Yin Pengyu, Wang Xiaohui, Yang Wei, and Cheng Kun. 2019. Glioma subregions segmentation with a discriminative adversarial regularized 3D Unet. In Proceedings of the 3rd International Symposium on Image Computing and Digital Medicine. 269–273.Google ScholarDigital Library
[116] Liu Ming-Yu, Huang Xun, Mallya Arun, Karras Tero, Aila Timo, Lehtinen Jaakko, and Kautz Jan. 2019. Few-shot unsupervised image-to-image translation. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 10551–10560.Google ScholarCross Ref
[117] Liu Shengfeng, Wang Yi, Yang Xin, Lei Baiying, Liu Li, Li Shawn Xiang, Ni Dong, and Wang Tianfu. 2019. Deep learning in medical ultrasound analysis: A review. Engineering 5, 2 (2019), 261–275.Google ScholarCross Ref
[118] Liu Xiao, Zhang Fanjin, Hou Zhenyu, Mian Li, Wang Zhaoyu, Zhang Jing, and Tang Jie. 2023. Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2023), 857–876.Google Scholar
[119] Liu Yanbin, Dwivedi Girish, Boussaid Farid, Sanfilippo Frank, Yamada Makoto, and Bennamoun Mohammed. 2023. Inflating 2D convolution weights for efficient generation of 3D medical images. Computer Methods and Programs in Biomedicine 240 (2023), 107685.Google Scholar
[120] Liu Y., Lee J., Park M., Kim S., Yang E., Hwang S. J., and Yang Y.. 2019. Learning to propagate labels: Transductive propagation network for few-shot learning. In Proceedings of the 7th International Conference on Learning Representations (ICLR ’19).Google Scholar
[121] Liu Yilin, Nacewicz Brendon M., Zhao Gengyan, Adluru Nagesh, Kirk Gregory R., Ferrazzano Peter A., Styner Martin A., and Alexander Andrew L.. 2020. A 3D fully convolutional neural network with top-down attention-guided refinement for accurate and robust automatic segmentation of amygdala and its subnuclei. Frontiers in Neuroscience 14 (2020), 260.Google ScholarCross Ref
[122] Liu Zhihua, Tong Lei, Chen Long, Jiang Zheheng, Zhou Feixiang, Zhang Qianni, Zhang Xiangrong, Jin Yaochu, and Zhou Huiyu. 2022. Deep learning based brain tumor segmentation: A survey. Complex & Intelligent Systems 94, 4 (2022), 1–26.Google Scholar
[123] Lundervold Alexander Selvikvåg and Lundervold Arvid. 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29, 2 (2019), 102–127.Google ScholarCross Ref
[124] Maier Oskar, Menze Bjoern H., Gablentz Janina von der, Häni Levin, Heinrich Mattias P., Liebrand Matthias, Winzeck Stefan, Basit Abdul, Bentley Paul, Chen Liang, et al. 2017. ISLES 2015—A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical Image Analysis 35 (2017), 250–269.Google ScholarCross Ref
[125] Marcus Daniel S., Wang Tracy H., Parker Jamie, Csernansky John G., Morris John C., and Buckner Randy L.. 2007. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19, 9 (2007), 1498–1507.Google ScholarDigital Library
[126] Marek Kenneth, Jennings Danna, Lasch Shirley, Siderowf Andrew, Tanner Caroline, Simuni Tanya, Coffey Chris, Kieburtz Karl, Flagg Emily, Chowdhury Sohini, et al. 2011. The Parkinson Progression Marker Initiative (PPMI). Progress in Neurobiology 95, 4 (2011), 629–635.Google ScholarCross Ref
[127] Mendrik Adriënne M., Vincken Koen L., Kuijf Hugo J., Breeuwer Marcel, Bouvy Willem H., Bresser Jeroen De, Alansary Amir, Bruijne Marleen De, Carass Aaron, El-Baz Ayman, et al. 2015. MRBrainS challenge: Online evaluation framework for brain image segmentation in 3T MRI scans. Computational Intelligence and Neuroscience 2015 (2015), 813696.Google ScholarDigital Library
[128] Mikolov Tomáš, Deoras Anoop, Povey Daniel, Burget Lukáš, and Černockỳ Jan. 2011. Strategies for training large scale neural network language models. In Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding. IEEE, Los Alamitos, CA, 196–201.Google ScholarCross Ref
[129] Milletari Fausto, Navab Nassir, and Ahmadi Seyed-Ahmad. 2016. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 4th International Conference on 3D Vision (3DV ’16). IEEE, Los Alamitos, CA, 565–571.Google ScholarCross Ref
[130] Mirza Mehdi and Osindero Simon. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).Google Scholar
[131] Mittal Anish, Soundararajan Rajiv, and Bovik Alan C.. 2012. Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20, 3 (2012), 209–212.Google ScholarCross Ref
[132] Mondal Arnab Kumar, Dolz Jose, and Desrosiers Christian. 2018. Few-shot 3D multi-modal medical image segmentation using generative adversarial learning. arXiv preprint arXiv:1810.12241 (2018).Google Scholar
[133] NAMIC Multimodality. 2023. NAMIC Multimodality Dataset Download Link. Retrieved December 27, 2023 from https://www.na-mic.org/wiki/DownloadsGoogle Scholar
[134] Mutke Matthias A., Madai Vince I., Samson-Himmelstjerna Federico C. von, Weber Olivier Zaro, Revankar Gajanan S., Martin Steve Z., Stengl Katharina L., Bauer Miriam, Hetzer Stefan, Günther Matthias, et al. 2014. Clinical evaluation of an arterial-spin-labeling product sequence in steno-occlusive disease of the brain. PLoS One 9, 2 (2014), e87143.Google ScholarCross Ref
[135] Myronenko Andriy. 2018. 3D MRI brain tumor segmentation using autoencoder regularization. In Proceedings of the International MICCAI Brainlesion Workshop. 311–320.Google Scholar
[136] Nan Yang, Ser Javier Del, Walsh Simon, Schönlieb Carola, Roberts Michael, Selby Ian, Howard Kit, Owen John, Neville Jon, Guiot Julien, et al. 2022. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. Information Fusion 82 (2022), 99–122.Google ScholarDigital Library
[137] Niyas S., Pawan S. J., Kumar M. Anand, and Rajan Jeny. 2022. Medical image segmentation with 3D convolutional neural networks: A survey. Neurocomputing 493 (2022), 397–413.Google ScholarDigital Library
[138] Oakes Robert S., Badger Troy J., Kholmovski Eugene G., Akoum Nazem, Burgon Nathan S., Fish Eric N., Blauer Joshua J. E., Rao Swati N., DiBella Edward V. R., Segerson Nathan M., et al. 2009. Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation 119, 13 (2009), 1758–1767.Google ScholarCross Ref
[139] Odena Augustus, Olah Christopher, and Shlens Jonathon. 2017. Conditional image synthesis with auxiliary classifier GANs. In Proceedings of the International Conference on Machine Learning. 2642–2651.Google Scholar
[140] Ojha Utkarsh, Li Yijun, Lu Jingwan, Efros Alexei A., Lee Yong Jae, Shechtman Eli, and Zhang Richard. 2021. Few-shot image generation via cross-domain correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10743–10752.Google ScholarCross Ref
[141] Ouyang Cheng, Biffi Carlo, Chen Chen, Kart Turkay, Qiu Huaqi, and Rueckert Daniel. 2022. Self-supervised learning for few-shot medical image segmentation. IEEE Transactions on Medical Imaging 41, 7 (2022), 1837–1848.Google ScholarCross Ref
[142] Özbey Muzaffer, Yurt Mahmut, Dar Salman Ul Hassan, and Çukur Tolga. 2020. Three dimensional MR image synthesis with progressive generative adversarial networks. arXiv preprint arXiv:2101.05218 (2020).Google Scholar
[143] Pan Yongsheng, Liu Mingxia, Lian Chunfeng, Zhou Tao, Xia Yong, and Shen Dinggang. 2018. Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer’s disease diagnosis. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 455–463.Google Scholar
[144] Paolacci Gabriele, Chandler Jesse, and Ipeirotis Panagiotis G.. 2010. Running experiments on Amazon Mechanical Turk. Judgment and Decision Making 5, 5 (2010), 411–419.Google ScholarCross Ref
[145] Pawlowski Nick, Castro Daniel Coelho de, and Glocker Ben. 2020. Deep structural causal models for tractable counterfactual inference. Advances in Neural Information Processing Systems 33 (2020), 857–869.Google Scholar
[146] Peng Bo, Liu Bingzheng, Bin Yi, Shen Lili, and Lei Jianjun. 2021. Multi-modality MR image synthesis via confidence-guided aggregation and cross-modality refinement. IEEE Journal of Biomedical and Health Informatics 26, 1 (2021), 27–35.Google ScholarCross Ref
[147] Peng Suting, Chen Wei, Sun Jiawei, and Liu Boqiang. 2020. Multi-scale 3D U-Nets: An approach to automatic segmentation of brain tumor. International Journal of Imaging Systems and Technology 30, 1 (2020), 5–17.Google ScholarCross Ref
[148] Perarnau Guim, Weijer Joost Van De, Raducanu Bogdan, and Álvarez Jose M.. 2016. Invertible conditional GANs for image editing. arXiv preprint arXiv:1611.06355 (2016).Google Scholar
[149] Pierson Harry A. and Gashler Michael S.. 2017. Deep learning in robotics: A review of recent research. Advanced Robotics 31, 16 (2017), 821–835.Google ScholarCross Ref
[150] Pinaya Walter H. L., Graham Mark S., Gray Robert, Costa Pedro F. Da, Tudosiu Petru-Daniel, Wright Paul, Mah Yee H., MacKinnon Andrew D., Teo James T., Jager Rolf, et al. 2022. Fast unsupervised brain anomaly detection and segmentation with diffusion models. arXiv preprint arXiv:2206.03461 (2022).Google Scholar
[151] Pinaya Walter H. L., Tudosiu Petru-Daniel, Gray Robert, Rees Geraint, Nachev Parashkev, Ourselin Sebastien, and Cardoso M. Jorge. 2022. Unsupervised brain imaging 3D anomaly detection and segmentation with transformers. Medical Image Analysis 79 (2022), 102475.Google ScholarCross Ref
[152] Pombo Guilherme, Gray Robert, Cardoso Jorge, Ourselin Sebastien, Rees Geraint, Ashburner John, and Nachev Parashkev. 2021. Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models. arXiv preprint arXiv:2111.14923 (2021).Google Scholar
[153] Pombo Guilherme, Gray Robert, Varsavsky Thomas, Ashburner John, and Nachev Parashkev. 2019. Bayesian volumetric autoregressive generative models for better semisupervised learning. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 429–437.Google ScholarDigital Library
[154] Puyol-Antón Esther, Chen Chen, Clough James R., Ruijsink Bram, Sidhu Baldeep S., Gould Justin, Porter Bradley, Elliott Marc, Mehta Vishal, Rueckert Daniel, et al. 2020. Interpretable deep models for cardiac resynchronisation therapy response prediction. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 284–293.Google ScholarDigital Library
[155] Qi Xiaoming, He Yuting, Yang Guanyu, Chen Yang, Yang Jian, Liu Wangyag, Zhu Yinsu, Xu Yi, Shu Huazhong, and Li Shuo. 2021. MVSGAN: Spatial-aware multi-view CMR fusion for accurate 3D left ventricular myocardium segmentation. IEEE Journal of Biomedical and Health Informatics 26, 5 (2021), 2264–2275.Google ScholarCross Ref
[156] Qiao Mengyun, Basaran Berke Doga, Qiu Huaqi, Wang Shuo, Guo Yi, Wang Yuanyuan, Matthews Paul M., Rueckert Daniel, and Bai Wenjia. 2022. Generative modelling of the ageing heart with cross-sectional imaging and clinical data. In Proceedings of the International Workshop on Statistical Atlases and Computational Models of the Heart. 3–12.Google ScholarDigital Library
[157] Qiao Mengyun, Wang Shuo, Qiu Huaqi, Marvao Antonio de, O’Regan Declan P., Rueckert Daniel, and Bai Wenjia. 2023. CHeart: A conditional spatio-temporal generative model for cardiac anatomy. arXiv preprint arXiv:2301.13098 (2023).Google Scholar
[158] Radau Perry, Lu Yingli, Connelly Kim, Paul Gideon, Dick Alexander J., and Wright Graham A.. 2009. Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS Journal. Retrieved December 22, 2023 from https:midasjournal.org/browse/publication/658Google Scholar
[159] Ramchandran Siddharth, Tikhonov Gleb, Lönnroth Otto, Tiikkainen Pekka, and Lähdesmäki Harri. 2022. Learning conditional variational autoencoders with missing covariates. arXiv preprint arXiv:2203.01218 (2022).Google Scholar
[160] Ramon Ubaldo, Hernandez Monica, and Mayordomo Elvira. 2022. LDDMM meets GANs: Generative adversarial networks for diffeomorphic registration. In Proceedings of the International Workshop on Biomedical Image Registration. 18–28.Google ScholarDigital Library
[161] Ran Maosong, Hu Jinrong, Chen Yang, Chen Hu, Sun Huaiqiang, Zhou Jiliu, and Zhang Yi. 2019. Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network. Medical Image Analysis 55 (2019), 165–180.Google ScholarCross Ref
[162] Rawal Atul, McCoy James, Rawat Danda B., Sadler Brian M., and Amant Robert St.. 2021. Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives. IEEE Transactions on Artificial Intelligence 3, 6 (2021), 852–866.Google ScholarCross Ref
[163] Reader Andrew J., Corda Guillaume, Mehranian Abolfazl, Costa-Luis Casper da, Ellis Sam, and Schnabel Julia A.. 2020. Deep learning for PET image reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences 5, 1 (2020), 1–25.Google ScholarCross Ref
[164] Reed Scott, Akata Zeynep, Yan Xinchen, Logeswaran Lajanugen, Schiele Bernt, and Lee Honglak. 2016. Generative adversarial text to image synthesis. In Proceedings of the International Conference on Machine Learning. 1060–1069.Google Scholar
[165] Rezaei Mina, Yang Haojin, and Meinel Christoph. 2018. voxel-GAN: Adversarial framework for learning imbalanced brain tumor segmentation. In Proceedings of the International MICCAI Brainlesion Workshop. 321–333.Google Scholar
[166] RIRE. 2022. RIRE Dataset. Retrieved December 22, 2023 from https://www.insight-journal.org/rire/Google Scholar
[167] Rohlfing Torsten, Brandt Robert, Menzel Randolf, and Jr Calvin R. Maurer. 2004. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21, 4 (2004), 1428–1442.Google ScholarCross Ref
[168] Ronneberger Olaf, Fischer Philipp, and Brox Thomas. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 234–241.Google ScholarCross Ref
[169] Rosca Mihaela, Lakshminarayanan Balaji, Warde-Farley David, and Mohamed Shakir. 2017. Variational approaches for auto-encoding generative adversarial networks. arXiv preprint arXiv:1706.04987 (2017).Google Scholar
[170] Sabour Sara, Frosst Nicholas, and Hinton Geoffrey E.. 2017. Dynamic routing between capsules. Advances in Neural Information Processing Systems 30 (2017), 1–11.Google Scholar
[171] Salehi Seyed Sadegh Mohseni, Erdogmus Deniz, and Gholipour Ali. 2017. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In Proceedings of the International Workshop on Machine Learning in Medical Imaging. 379–387.Google ScholarCross Ref
[172] Salimans Tim, Goodfellow Ian, Zaremba Wojciech, Cheung Vicki, Radford Alec, and Chen Xi. 2016. Improved techniques for training GANs. Advances in Neural Information Processing Systems 29 (2016), 1–11.Google Scholar
[173] Saxena Divya and Cao Jiannong. 2021. Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Computing Surveys 54, 3 (2021), 1–42.Google ScholarDigital Library
[174] Segato Alice, Corbetta Valentina, Marzo Marco Di, Pozzi Luca, and Momi Elena De. 2020. Data augmentation of 3D brain environment using deep convolutional refined auto-encoding alpha GAN. IEEE Transactions on Medical Robotics and Bionics 3, 1 (2020), 269–272.Google ScholarCross Ref
[175] Shapey Jonathan, Kujawa Aaron, Dorent Reuben, Wang Guotai, Dimitriadis Alexis, Grishchuk Diana, Paddick Ian, Kitchen Neil, Bradford Robert, Saeed Shakeel R., et al. 2021. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm. Scientific Data 8, 1 (2021), 1–6.Google ScholarCross Ref
[176] Shattuck David W., Mirza Mubeena, Adisetiyo Vitria, Hojatkashani Cornelius, Salamon Georges, Narr Katherine L., Poldrack Russell A., Bilder Robert M., and Toga Arthur W.. 2008. Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage 39, 3 (2008), 1064–1080.Google ScholarCross Ref
[177] Sikka Apoorva, Skand, Jitender Singh Virk, Deepti R. Bathula. 2021. MRI to PET cross-modality translation using globally and locally aware GAN (GLA-GAN) for multi-modal diagnosis of Alzheimer’s disease. arXiv preprint arXiv:2108.02160 (2021).Google Scholar
[178] Simpson Amber L., Antonelli Michela, Bakas Spyridon, Bilello Michel, Farahani Keyvan, Ginneken Bram Van, Kopp-Schneider Annette, Landman Bennett A., Litjens Geert, Menze Bjoern, et al. 2019. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019).Google Scholar
[179] Simpson Robin, Keegan Jennifer, and Firmin David. 2013. Efficient and reproducible high resolution spiral myocardial phase velocity mapping of the entire cardiac cycle. Journal of Cardiovascular Magnetic Resonance 15, 1 (2013), 1–14.Google ScholarCross Ref
[180] Simpson Robin, Keegan Jennifer, Gatehouse Peter, Hansen Michael, and Firmin David. 2014. Spiral tissue phase velocity mapping in a breath-hold with non-Cartesian SENSE. Magnetic Resonance in Medicine 72, 3 (2014), 659–668.Google ScholarCross Ref
[181] Singh Satya P., Wang Lipo, Gupta Sukrit, Goli Haveesh, Padmanabhan Parasuraman, and Gulyás Balázs. 2020. 3D deep learning on medical images: A review. Sensors 20, 18 (2020), 5097.Google ScholarCross Ref
[182] Sohl-Dickstein Jascha, Weiss Eric, Maheswaranathan Niru, and Ganguli Surya. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International Conference on Machine Learning. 2256–2265.Google Scholar
[183] Sohn Kihyuk, Lee Honglak, and Yan Xinchen. 2015. Learning structured output representation using deep conditional generative models. Advances in Neural Information Processing Systems 28 (2015), 1–9.Google Scholar
[184] Srivastava Akash, Valkov Lazar, Russell Chris, Gutmann Michael U., and Sutton Charles. 2017. VEEGAN: Reducing mode collapse in GANs using implicit variational learning. Advances in Neural Information Processing Systems 30 (2017), 1–11.Google Scholar
[185] Subramaniam Pooja, Kossen Tabea, Ritter Kerstin, Hennemuth Anja, Hildebrand Kristian, Hilbert Adam, Sobesky Jan, Livne Michelle, Galinovic Ivana, Khalil Ahmed A., et al. 2022. Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks. Medical Image Analysis 78 (2022), 102396.Google ScholarCross Ref
[186] Sudlow Cathie, Gallacher John, Allen Naomi, Beral Valerie, Burton Paul, Danesh John, Downey Paul, Elliott Paul, Green Jane, Landray Martin, et al. 2015. UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Medicine 12, 3 (2015), e1001779.Google ScholarCross Ref
[187] Suinesiaputra Avan, Cowan Brett R., Al-Agamy Ahmed O., Elattar Mustafa A., Ayache Nicholas, Fahmy Ahmed S., Khalifa Ayman M., Medrano-Gracia Pau, Jolly Marie-Pierre, Kadish Alan H., et al. 2014. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Medical Image Analysis 18, 1 (2014), 50–62.Google ScholarCross Ref
[188] Sun Li, Chen Junxiang, Xu Yanwu, Gong Mingming, Yu Ke, and Batmanghelich Kayhan. 2022. Hierarchical amortized GAN for 3D high resolution medical image synthesis. IEEE Journal of Biomedical and Health Informatics 26, 8 (2022), 3966–3975.Google ScholarCross Ref
[189] Sünderhauf Niko, Brock Oliver, Scheirer Walter, Hadsell Raia, Fox Dieter, Leitner Jürgen, Upcroft Ben, Abbeel Pieter, Burgard Wolfram, Milford Michael, et al. 2018. The limits and potentials of deep learning for robotics. International Journal of Robotics Research 37, 4-5 (2018), 405–420.Google ScholarCross Ref
[190] Sutskever Ilya, Vinyals Oriol, and Le Quoc V.. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 27 (2014), 1–9.Google Scholar
[191] Tao Song and Wang Jia. 2020. Alleviation of gradient exploding in GANs: Fake can be real. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1191–1200.Google ScholarCross Ref
[192] Thanh-Tung Hoang and Tran Truyen. 2020. Catastrophic forgetting and mode collapse in GANs. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN ’20). IEEE, Los Alamitos, CA, 1–10.Google ScholarCross Ref
[193] Tompson Jonathan, Goroshin Ross, Jain Arjun, LeCun Yann, and Bregler Christoph. 2015. Efficient object localization using convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 648–656.Google ScholarCross Ref
[194] Treder Matthias S., Codrai Ryan, and Tsvetanov Kamen A.. 2022. Quality assessment of anatomical MRI images from generative adversarial networks: Human assessment and image quality metrics. Journal of Neuroscience Methods 374 (2022), 109579.Google ScholarCross Ref
[195] Tritrong Nontawat, Rewatbowornwong Pitchaporn, and Suwajanakorn Supasorn. 2021. Repurposing GANs for one-shot semantic part segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4475–4485.Google ScholarCross Ref
[196] Ullah Faizad, Ansari Shahab U., Hanif Muhammad, Ayari Mohamed Arselene, Chowdhury Muhammad Enamul Hoque, Khandakar Amith Abdullah, and Khan Muhammad Salman. 2021. Brain MR image enhancement for tumor segmentation using 3D U-Net. Sensors 21, 22 (2021), 7528.Google ScholarCross Ref
[197] Uzunova Hristina, Schultz Sandra, Handels Heinz, and Ehrhardt Jan. 2019. Unsupervised pathology detection in medical images using conditional variational autoencoders. International Journal of Computer Assisted Radiology and Surgery 14, 3 (2019), 451–461.Google ScholarCross Ref
[198] Oord Aaron Van den, Kalchbrenner Nal, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016. Conditional image generation with PixelCNN decoders. Advances in Neural Information Processing Systems 29 (2016), 1–9.Google Scholar
[199] Oord Aaron Van Den, Oriol Vinyals, and Koray Kavukcuoglu. 2017. Neural discrete representation learning. Advances in Neural Information Processing Systems 30 (2017), 1–10.Google Scholar
[200] Ouderaa Tycho F. A. van der and Worrall Daniel E.. 2019. Reversible GANs for memory-efficient image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4720–4728.Google Scholar
[201] Essen David C. Van, Smith Stephen M., Barch Deanna M., Behrens Timothy E. J., Yacoub Essa, and Kamil Ugurbil; WU-Minn HCP Consortium. 2013. The WU-Minn Human Connectome Project: An overview. NeuroImage 80 (2013), 62–79.Google ScholarCross Ref
[202] Oord Aaron Van, Kalchbrenner Nal, and Kavukcuoglu Koray. 2016. Pixel recurrent neural networks. In Proceedings of the International Conference on Machine Learning. 1747–1756.Google Scholar
[203] Vaswani Ashish, Shazeer Noam, Parmar Niki, Uszkoreit Jakob, Jones Llion, Gomez Aidan N., Kaiser Łukasz, and Polosukhin Illia. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 1–11.Google Scholar
[204] Vesal Sulaiman, Maier Andreas, and Ravikumar Nishant. 2020. Fully automated 3D cardiac MRI localisation and segmentation using deep neural networks. Journal of Imaging 6, 7 (2020), 65.Google ScholarCross Ref
[205] Vincent Pascal, Larochelle Hugo, Bengio Yoshua, and Manzagol Pierre-Antoine. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning. 1096–1103.Google ScholarDigital Library
[206] Vincent Pascal, Larochelle Hugo, Lajoie Isabelle, Bengio Yoshua, Manzagol Pierre-Antoine, and Bottou Léon. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11, 12 (2010), 1–38.Google Scholar
[207] Volokitin Anna, Erdil Ertunc, Karani Neerav, Tezcan Kerem Can, Chen Xiaoran, Gool Luc Van, and Konukoglu Ender. 2020. Modelling the distribution of 3D brain MRI using a 2D slice VAE. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 657–666.Google ScholarDigital Library
[208] Wang Chengjia, Papanastasiou Giorgos, Tsaftaris Sotirios, Yang Guang, Gray Calum, Newby David, Macnaught Gillian, and MacGillivray Tom. 2019. TPSDicyc: Improved deformation invariant cross-domain medical image synthesis. In Proceedings of the 2nd International Workshop on Machine Learning for Medical Image Reconstruction (MLMIR ’19). 245–254.Google Scholar
[209] Wang Chengjia, Yang Guang, Papanastasiou Giorgos, Tsaftaris Sotirios A., Newby David E., Gray Calum, Macnaught Gillian, and MacGillivray Tom J.. 2021. DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis. Information Fusion 67 (2021), 147–160.Google ScholarCross Ref
[210] Wang Liansheng, Xie Cong, and Zeng Nianyin. 2019. RP-Net: A 3D convolutional neural network for brain segmentation from magnetic resonance imaging. IEEE Access 7 (2019), 39670–39679.Google ScholarCross Ref
[211] Wang Xintao, Yu Ke, Wu Shixiang, Gu Jinjin, Liu Yihao, Dong Chao, Qiao Yu, and Loy Chen Change. 2018. ESRGAN: Enhanced super-resolution generative adversarial networks. In Proceedings of the European Conference on Computer Vision Workshops (ECCV ’18).Google Scholar
[212] Wang Yaqing, Yao Quanming, Kwok James T., and Ni Lionel M.. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys 53, 3 (2020), 1–34.Google ScholarDigital Library
[213] Wang Yan, Yu Biting, Wang Lei, Zu Chen, Lalush David S., Lin Weili, Wu Xi, Zhou Jiliu, Shen Dinggang, and Zhou Luping. 2018. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. NeuroImage 174 (2018), 550–562.Google ScholarCross Ref
[214] Wang Yan, Zhou Luping, Yu Biting, Wang Lei, Zu Chen, Lalush David S., Lin Weili, Wu Xi, Zhou Jiliu, and Shen Dinggang. 2018. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Transactions on Medical Imaging 38, 6 (2018), 1328–1339.Google ScholarCross Ref
[215] Weiner Michael W., Veitch Dallas P., Aisen Paul S., Beckett Laurel A., Cairns Nigel J., Green Robert C., Harvey Danielle, Jr. Clifford R. Jack, Jagust William, Morris John C., et al. 2017. The Alzheimer’s Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement. Alzheimer’s & Dementia 13, 5 (2017), 561–571.Google ScholarCross Ref
[216] Wolterink Jelmer M., Leiner Tim, Viergever Max A., and Išgum Ivana. 2017. Generative adversarial networks for noise reduction in low-dose CT. IEEE Transactions on Medical Imaging 36, 12 (2017), 2536–2545.Google ScholarCross Ref
[217] Würfl Tobias, Ghesu Florin C., Christlein Vincent, and Maier Andreas. 2016. Deep learning computed tomography. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 432–440.Google ScholarDigital Library
[218] Xia Tian, Chartsias Agisilaos, Tsaftaris Sotirios A., and Initiative Alzheimer’s Disease Neuroimaging. 2019. Consistent brain ageing synthesis. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI ’19). 750–758.Google Scholar
[219] Xiao Haonan, Teng Xinzhi, Liu Chenyang, Li Tian, Ren Ge, Yang Ruijie, Shen Dinggang, and Cai Jing. 2021. A review of deep learning-based three-dimensional medical image registration methods. Quantitative Imaging in Medicine and Surgery 11, 12 (2021), 4895.Google ScholarCross Ref
[220] Xing Xiaodan, Ser Javier Del, Wu Yinzhe, Li Yang, Xia Jun, Lei Xu, Firmin David, Gatehouse Peter, and Yang Guang. 2023. HDL: Hybrid deep learning for the synthesis of myocardial velocity maps in Digital Twins for cardiac analysis. IEEE Journal of Biomedical and Health Informatics 27, 10 (2023), 5134–5142.Google ScholarCross Ref
[221] Xing Xiaodan, Huang Jiahao, Nan Yang, Wu Yinzhe, Wang Chengjia, Gao Zhifan, Walsh Simon, and Yang Guang. 2022. CS 2: A controllable and simultaneous synthesizer of images and annotations with minimal human intervention. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 3–12.Google ScholarDigital Library
[222] Xing Xiaodan, Wu Huanjun, Wang Lichao, Stenson Iain, Yong May, Ser Javier Del, Walsh Simon, and Yang Guang. 2022. Non-imaging medical data synthesis for trustworthy AI: A comprehensive survey. arXiv preprint arXiv:2209.09239 (2022).Google Scholar
[223] Xing Xiaodan, Wu Yinzhe, Firmin David, Gatehouse Peter, and Yang Guang. 2022. Synthetic velocity mapping cardiac MRI coupled with automated left ventricle segmentation. In Medical Imaging 2022: Image Processing, Vol. 12032. SPIE, 781–785.Google Scholar
[224] Xiong Xiangyu, Ding Yan, Sun Chuanqi, Zhang Zhuoneng, Guan Xiuhong, Zhang Tianjing, Chen Hao, Liu Hongyan, Cheng Zhangbo, Zhao Lei, et al. 2022. A cascaded multi-task generative framework for detecting aortic dissection on 3D non-contrast-enhanced computed tomography. IEEE Journal of Biomedical and Health Informatics 26, 10 (2022), 5177–5188.Google ScholarCross Ref
[225] Xiong Zhaohan, Xia Qing, Hu Zhiqiang, Huang Ning, Bian Cheng, Zheng Yefeng, Vesal Sulaiman, Ravikumar Nishant, Maier Andreas, Yang Xin, et al. 2021. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Medical Image Analysis 67 (2021), 101832.Google ScholarCross Ref
[226] Xu Chenchu, Howey Joanne, Ohorodnyk Pavlo, Roth Mike, Zhang Heye, and Li Shuo. 2020. Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning. Medical Image Analysis 59 (2020), 101568.Google ScholarCross Ref
[227] Xu Chenchu, Xu Lei, Brahm Gary, Zhang Heye, and Li Shuo. 2018. MuTGAN: Simultaneous segmentation and quantification of myocardial infarction without contrast agents via joint adversarial learning. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 525–534.Google ScholarDigital Library
[228] Xu Chenchu, Xu Lei, Ohorodnyk Pavlo, Roth Mike, Chen Bo, and Li Shuo. 2020. Contrast agent-free synthesis and segmentation of ischemic heart disease images using progressive sequential causal GANs. Medical Image Analysis 62 (2020), 101668.Google ScholarCross Ref
[229] Yan Xinchen, Yang Jimei, Sohn Kihyuk, and Lee Honglak. 2016. Attribute2Image: Conditional image generation from visual attributes. In Proceedings of the European Conference on Computer Vision. 776–791.Google ScholarCross Ref
[230] Yang Dong, Liu Bo, Axel Leon, and Metaxas Dimitris. 2018. 3D LV probabilistic segmentation in cardiac MRI using generative adversarial network. In Proceedings of the International Workshop on Statistical Atlases and Computational Models of the Heart. 181–190.Google Scholar
[231] Yang Huan, Lu Xianling, Wang Shui-Hua, Lu Zhihai, Yao Jian, Jiang Yizhang, and Qian Pengjiang. 2021. Synthesizing multi-contrast MR images via novel 3D conditional variational auto-encoding GAN. Mobile Networks and Applications 26, 1 (2021), 415–424.Google ScholarCross Ref
[232] Yang Jiancheng, Shi Rui, Wei Donglai, Liu Zequan, Zhao Lin, Ke Bilian, Pfister Hanspeter, and Ni Bingbing. 2021. MedMNIST v2: A large-scale lightweight benchmark for 2D and 3D biomedical image classification. arXiv preprint arXiv:2110.14795 (2021).Google Scholar
[233] Yao Kai, Su Zixian, Huang Kaizhu, Yang Xi, Sun Jie, Hussain Amir, and Coenen Frans. 2022. A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation. IEEE Journal of Biomedical and Health Informatics 26, 10 (2022), 4976–4986.Google ScholarCross Ref
[234] Yi Xin, Walia Ekta, and Babyn Paul. 2019. Generative adversarial network in medical imaging: A review. Medical Image Analysis 58 (2019), 101552.Google ScholarCross Ref
[235] Yu Biting, Zhou Luping, Wang Lei, Fripp Jurgen, and Bourgeat Pierrick. 2018. 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI ’18). IEEE, Los Alamitos, CA, 626–630.Google ScholarCross Ref
[236] Yu Biting, Zhou Luping, Wang Lei, Shi Yinghuan, Fripp Jurgen, and Bourgeat Pierrick. 2019. Ea-GANs: Edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Transactions on Medical Imaging 38, 7 (2019), 1750–1762.Google ScholarCross Ref
[237] Yu Biting, Zhou Luping, Wang Lei, Shi Yinghuan, Fripp Jurgen, and Bourgeat Pierrick. 2020. Sample-adaptive GANs: Linking global and local mappings for cross-modality MR image synthesis. IEEE Transactions on Medical Imaging 39, 7 (2020), 2339–2350.Google ScholarCross Ref
[238] Yu Lequan, Wang Shujun, Li Xiaomeng, Fu Chi-Wing, and Heng Pheng-Ann. 2019. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 605–613.Google ScholarDigital Library
[239] Yuan Wenguang, Wei Jia, Wang Jiabing, Ma Qianli, and Tasdizen Tolga. 2020. Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images. Medical Image Analysis 64 (2020), 101731.Google ScholarCross Ref
[240] Yurt Mahmut, Özbey Muzaffer, Dar Salman U. H., Tinaz Berk, Oguz Kader K., and Çukur Tolga. 2022. Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery. Medical Image Analysis 78 (2022), 102429.Google ScholarCross Ref
[241] Zbontar Jure, Knoll Florian, Sriram Anuroop, Murrell Tullie, Huang Zhengnan, Muckley Matthew J., Defazio Aaron, Stern Ruben, Johnson Patricia, Bruno Mary, et al. 2018. fastMRI: An open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839 (2018).Google Scholar
[242] Zhang Dingwen, Huang Guohai, Zhang Qiang, Han Jungong, Han Junwei, and Yu Yizhou. 2021. Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110 (2021), 107562.Google ScholarCross Ref
[243] Zhang Han, Xu Tao, Li Hongsheng, Zhang Shaoting, Wang Xiaogang, Huang Xiaolei, and Metaxas Dimitris N.. 2017. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision. 5907–5915.Google ScholarCross Ref
[244] Zhang Han, Xu Tao, Li Hongsheng, Zhang Shaoting, Wang Xiaogang, Huang Xiaolei, and Metaxas Dimitris N.. 2018. StackGAN++: Realistic image synthesis with stacked generative adversarial networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 8 (2018), 1947–1962.Google ScholarCross Ref
[245] Zhang Le, Gooya Ali, and Frangi Alejandro F.. 2017. Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets. In Proceedings of the International Workshop on Simulation and Synthesis in Medical Imaging. 61–68.Google ScholarCross Ref
[246] Zhang Xiaoyue, Jian Weijian, Chen Yu, and Yang Shihting. 2020. Deform-GAN: An unsupervised learning model for deformable registration. arXiv preprint arXiv:2002.11430 (2020).Google Scholar
[247] Zhang Zizhao, Yang Lin, and Zheng Yefeng. 2018. Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9242–9251.Google ScholarCross Ref
[248] Zhao Long, Zhang Zizhao, Chen Ting, Metaxas Dimitris, and Zhang Han. 2021. Improved transformer for high-resolution GANs. Advances in Neural Information Processing Systems 34 (2021), 18367–18380.Google Scholar
[249] Zhao Pu, Pan Hong, and Xia Siyu. 2021. MRI-Trans-GAN: 3D MRI cross-modality translation. In Proceedings of the 2021 40th Chinese Control Conference (CCC ’21). IEEE, Los Alamitos, CA, 7229–7234.Google ScholarCross Ref
[250] Zhao Shengyu, Lau Tingfung, Luo Ji, Eric I., Chang Chao, and Xu Yan. 2019. Unsupervised 3D end-to-end medical image registration with volume tweening network. IEEE Journal of Biomedical and Health Informatics 24, 5 (2019), 1394–1404.Google ScholarCross Ref
[251] Zhao Zhengli, Singh Sameer, Lee Honglak, Zhang Zizhao, Odena Augustus, and Zhang Han. 2021. Improved consistency regularization for GANs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 11033–11041.Google ScholarCross Ref
[252] Zhou Yang, Yang Zhiwen, Zhang Hui, Eric I., Chang Chao, Fan Yubo, and Xu Yan. 2022. 3D segmentation guided style-based generative adversarial networks for PET synthesis. IEEE Transactions on Medical Imaging 41, 8 (2022), 2092–2104.Google Scholar
[253] Zhu Jun-Yan, Park Taesung, Isola Phillip, and Efros Alexei A.. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision. 2223–2232.Google ScholarCross Ref
[254] Zhu Xingxing, Huang Zhiwen, Ding Mingyue, and Zhang Xuming. 2022. Non-rigid multi-modal brain image registration based on two-stage generative adversarial nets. Neurocomputing 505 (2022), 44–57.Google ScholarDigital Library
[255] Zhu Zhenyu, Cao Yiqin, Qin Chenchen, Rao Yi, Lin Di, Dou Qi, Ni Dong, and Wang Yi. 2021. Joint affine and deformable three-dimensional networks for brain MRI registration. Medical Physics 48, 3 (2021), 1182–1196.Google ScholarCross Ref
[256] Zhuang Xiahai and Shen Juan. 2016. Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Medical Image Analysis 31 (2016), 77–87.Google ScholarCross Ref

Index Terms

3D Brain and Heart Volume Generative Models: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging

Recommendations

Mixed-membership naive Bayes models

In recent years, mixture models have found widespread usage in discovering latent cluster structure from data. A popular special case of finite mixture models is the family of naive Bayes (NB) models, where the probability of a feature vector factorizes ...
Read More
Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion
Abstract
In this paper, we propose a Bayesian switching dynamical model for segmentation of 3D pose data over time that uncovers interpretable patterns in the data and is generative. Our model decomposes highly correlated skeleton data into a set of few ...
Read More
A comprehensive survey and analysis of generative models in machine learning
Abstract
Generative models have been in existence for many decades. In the field of machine learning, we come across many scenarios when directly learning a target is intractable through discriminative models, and in such cases the joint ...
Highlights
- Generative models generate joint distribution of target and training data.
- ...
Read More

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Published in
ACM Computing Surveys Volume 56, Issue 6
June 2024
963 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613600
Editor:
Albert Zomaya
University of Sydney, Australia
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 22 January 2024
- Online AM: 19 December 2023
- Accepted: 1 December 2023
- Revised: 26 August 2023
- Received: 12 October 2022
Published in csur Volume 56, Issue 6

Permissions
Request permissions about this article.
Request Permissions

Check for updates
Author Tags
Generative models
three-dimensional
medical images
brain and heart
Qualifiers
- survey
Conference
Funding Sources
Other Metrics
View Article Metrics

Article Metrics
- 0
  Total Citations
  View Citations
- 470
  Total Downloads
- Downloads (Last 12 months)470
- Downloads (Last 6 weeks)126
Other Metrics
View Author Metrics
Cited By
This publication has not been cited yet

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

View Full Text

3D Brain and Heart Volume Generative Models: A Survey

ACM Computing Surveys

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

Mixed-membership naive Bayes models

Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion

A comprehensive survey and analysis of generative models in machine learning