Abstract
In the field of image analysis used for diagnostic processes, domain shifts constitute a significant obstacle. Domain shifts lead to an incompatibility of an otherwise well-performing AI model for image segmentation. Accordingly, if two different machines image the same tissue, the model may provide better results for one of the two images depending on the similarity of the image data compared to the training data for generating the AI model. In this paper, we analyzed how the input images of a neural network have to be adapted to provide better segmentation results for images which are previously not compatible with the used model. Therefore, we developed two approaches to increase a model’s segmentation quality for a dataset with initially poor results. The first approach is based on heuristic optimization and creates a set of image processing algorithms for the data adaptation. Our algorithm selects the best combination of algorithms and generates the most suitable parameters for them regarding the resulting segmentation quality. The second approach uses an additional neural network for learning the incompatible dataset’s recoloring based on the resulting segmentation quality. Both methods increase the segmentation quality significantly without the need for changes to the segmentation model itself.
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References
Haghofer, A.: Heuristic domain shift adaptation for the analysis of blood vessel images. Paper presented at BIOSTEC 2022. https://www.insticc.org/node/TechnicalProgram/biostec/2022/presentationDetails/110288
Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0
Radosavovic, I., Kosaraju, R., Girshick, R., He, K., Dollár, P.: Designing network design spaces. In: Proceedings of the IEEE Computer Society Conference On Computer Vision And Pattern Recognition (2020)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Iakubovskii, P.: Segmentation Models Pytorch. GitHub Repository (2019). https://github.com/qubvel/segmentation_models.pytorch
Getreuer, P.: A Survey of Gaussian Convolution Algorithms. Image Processing On Line (2013)
Szeliski, R.: Computer vision: algorithms and applications. Choice Reviews Online (2011)
Haghofer, A., Dorl, S., Oszwald, A., Breuss, J., Jacak, J., Winkler, S.M.: Evolutionary optimization of image processing for cell detection in microscopy images. Soft Comput. 24(23), 17847–17862 (2020). https://doi.org/10.1007/s00500-020-05033-0
Rechenberg, I.: Evolutionsstrategie 94. Frommann-Holzboog (1994)
Dice, L.: Measures of the amount of ecologic association between species. Ecology (1945)
Turchenko, V., Chalmers, E., Luczak, A.: A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe (2017)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8024–8035 (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Falcon, W., et al.: Pytorch lightning. GitHub, vol. 3 (2019). https://github.com/pytorchlightning/pytorch-lightning
Green-Armytage, P.: Complementary Colors. Encyclopedia Of Color Science And Technology, pp. 523–535 (2016). https://doi.org/10.1007/978-1-4419-8071-7_264
OpenCV Open Source Computer Vision Library (2015)
Deryugina, E., Quigley, J.: Chapter 2 Chick Embryo chorioallantoic membrane models to quantify angiogenesis induced by inflammatory and tumor cells or purified effector molecules. Methods Enzymol. 444, 21–41 (2008)
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P., Shao, L.: Structure preserving stain normalization of histopathology images using self supervised semantic guidance. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 309–319. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_30
Roy, G., et al.: Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation (2021)
Acknowledgements
The work described in this paper was supported by the Center of Excellence for Technical Innovation in Medicine (TIMED), the Dissertation Programme of the University of Applied Sciences Upper Austria, and the Austrian Research Promotion Agency (FFG, project no. 881547).
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Haghofer, A. et al. (2023). Automated Data Adaptation for the Segmentation of Blood Vessels. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_4
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