Abstract
The precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising performance in the segmentation of medical images. However, the small dimension of available datasets is affecting the biomedical imaging domain significantly and has a huge impact in training of deep learning models. In this paper we try to address this issue by iteratively augmenting the dataset with auxiliary task-based information. This is obtained by introducing a recursive training approach, where a new set of segmented images is generated at each iteration and then concatenated with the original input data as organ attention maps. In the experimental evaluation two different datasets were tested and the results produced from the proposed approach have shown significant improvements in organ segmentation as compared to a standard non-recursive approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akbar, M.U., Aslani, S., Murino, V., Sona, D.: Multiple organs segmentation in abdomen CT scans using a cascade of CNNs. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11751, pp. 509–516. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30642-7_46
Chen, H., et al.: A recursive ensemble organ segmentation (reos) framework: application in brain radiotherapy. Phys. Med. Biol. 64(2), 025015 (2019)
Gerazov, B., Conceicao, R.C.: Deep learning for tumour classification in homogeneous breast tissue in medical microwave imaging. In: IEEE EUROCON 2017–17th International Conference on Smart Technologies, pp. 564–569. IEEE (2017)
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Mansoor, A., Cerrolaza, J.J., Perez, G., Biggs, E., Nino, G., Linguraru, M.G.: Marginal shape deep learning: applications to pediatric lung field segmentation. In: Medical Imaging 2017: Image Processing. vol. 10133, p. 1013304. International Society for Optics and Photonics (2017)
Pawlowski, N., et al.: DLTK: state of the art reference implementations for deep learning on medical images. arXiv preprint arXiv:1711.06853 (2017)
Sevastopolsky, A.: Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network. Pattern Recogn. Image Anal. 27(3), 618–624 (2017)
Shahab, A., et al.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. NeuroImage 196, 1–15 (2019)
Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 588–599. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19992-4_46
Shin, H.C., Orton, M.R., Collins, D.J., Doran, S.J., Leach, M.O.: Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2012)
Sun, C., et al.: Automatic segmentation of liver tumors from multiphase contrast-enhanced ct images based on fcns. Artif. Intell. Med. 83, 58–66 (2017)
Tu, Z.: Auto-context and its application to high-level vision tasks. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Wang, Y., Zhou, Y., Shen, W., Park, S., Fishman, E.K., Yuille, A.L.: Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med. Image Anal. 55, 88–102 (2019)
Zou, Y., Li, L., Wang, Y., Yu, J., Li, Y., Deng, W.: Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), pp. 1274–1278. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Akbar, M.U., Yamin, M.A., Murino, V., Sona, D. (2021). Organ Segmentation with Recursive Data Augmentation for Deep Models. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-68763-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68762-5
Online ISBN: 978-3-030-68763-2
eBook Packages: Computer ScienceComputer Science (R0)
