Abstract
Machine learning methods heavily rely on the availability of large annotated datasets of a certain domain for training. However, freely available datasets of patients with pathologies rarely contain annotations of normal structures, thus cannot be used as ground truth for various image processing methods. To overcome this issue, we propose a topology preserving unpaired domain translation method, including an explicit pathology integration to generate annotated ground truth data of pathological domains. Moreover, we integrate a novel inverse probabilistic approach to generate deformations of the surrounding caused by pathological tissue. Our experiments show the necessity for annotated pathological data for algorithm evaluation. Furthermore, when training neural networks on healthy data and testing on real pathological images, the results are strongly impaired. By generating training data with pathologies using the proposed method, the performance of segmentation and registration methods increases significantly. The best results are achieved by also integrating pathology-induced tissue deformations.
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Notes
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For more details, see our code at: https://github.com/hristina-uzunova/TumorMassEffect.
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Uzunova, H., Ehrhardt, J., Handels, H. (2020). Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_49
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