An Auto-Encoder Strategy for Adaptive Image Segmentation

Evan M. Yu, Juan Eugenio Iglesias, Adrian V. Dalca, Mert R. Sabuncu
; Proceedings of the Third Conference on Medical Imaging with Deep Learning, PMLR 121:881-891, 2020.

Abstract

Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel-level labels. However, obtaining segmentations of anatomical regions on a large number of cases can be prohibitively expensive. Thus there is a strong need for deep learning-based segmentation tools that do not require heavy supervision and can continuously adapt. In this paper, we propose a novel perspective of segmentation as a discrete representation learning problem, and present a variational autoencoder segmentation strategy that is flexible and adaptive. Our method, called Segmentation Auto-Encoder (SAE), leverages all available unlabeled scans and merely requires a segmentation prior, which can be \textit{a single unpaired} segmentation image. In experiments, we apply SAE to brain MRI scans. Our results show that SAE can produce good quality segmentations, particularly when the prior is good. We demonstrate that a Markov Random Field prior can yield significantly better results than a spatially independent prior. Our code is freely available at: {https://github.com/evanmy/sae}.

Cite this Paper


BibTeX
@InProceedings{pmlr-v121-yu20a, title = {An Auto-Encoder Strategy for Adaptive Image Segmentation}, author = {Yu, Evan M. and Iglesias, Juan Eugenio and Dalca, Adrian V. and Sabuncu, Mert R.}, pages = {881--891}, year = {2020}, editor = {Tal Arbel and Ismail Ben Ayed and Marleen de Bruijne and Maxime Descoteaux and Herve Lombaert and Christopher Pal}, volume = {121}, series = {Proceedings of Machine Learning Research}, address = {Montreal, QC, Canada}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v121/yu20a/yu20a.pdf}, url = {http://proceedings.mlr.press/v121/yu20a.html}, abstract = {Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel-level labels. However, obtaining segmentations of anatomical regions on a large number of cases can be prohibitively expensive. Thus there is a strong need for deep learning-based segmentation tools that do not require heavy supervision and can continuously adapt. In this paper, we propose a novel perspective of segmentation as a discrete representation learning problem, and present a variational autoencoder segmentation strategy that is flexible and adaptive. Our method, called Segmentation Auto-Encoder (SAE), leverages all available unlabeled scans and merely requires a segmentation prior, which can be \textit{a single unpaired} segmentation image. In experiments, we apply SAE to brain MRI scans. Our results show that SAE can produce good quality segmentations, particularly when the prior is good. We demonstrate that a Markov Random Field prior can yield significantly better results than a spatially independent prior. Our code is freely available at: {https://github.com/evanmy/sae}.} }
Endnote
%0 Conference Paper %T An Auto-Encoder Strategy for Adaptive Image Segmentation %A Evan M. Yu %A Juan Eugenio Iglesias %A Adrian V. Dalca %A Mert R. Sabuncu %B Proceedings of the Third Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2020 %E Tal Arbel %E Ismail Ben Ayed %E Marleen de Bruijne %E Maxime Descoteaux %E Herve Lombaert %E Christopher Pal %F pmlr-v121-yu20a %I PMLR %J Proceedings of Machine Learning Research %P 881--891 %U http://proceedings.mlr.press %V 121 %W PMLR %X Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel-level labels. However, obtaining segmentations of anatomical regions on a large number of cases can be prohibitively expensive. Thus there is a strong need for deep learning-based segmentation tools that do not require heavy supervision and can continuously adapt. In this paper, we propose a novel perspective of segmentation as a discrete representation learning problem, and present a variational autoencoder segmentation strategy that is flexible and adaptive. Our method, called Segmentation Auto-Encoder (SAE), leverages all available unlabeled scans and merely requires a segmentation prior, which can be \textit{a single unpaired} segmentation image. In experiments, we apply SAE to brain MRI scans. Our results show that SAE can produce good quality segmentations, particularly when the prior is good. We demonstrate that a Markov Random Field prior can yield significantly better results than a spatially independent prior. Our code is freely available at: {https://github.com/evanmy/sae}.
APA
Yu, E.M., Iglesias, J.E., Dalca, A.V. & Sabuncu, M.R.. (2020). An Auto-Encoder Strategy for Adaptive Image Segmentation. Proceedings of the Third Conference on Medical Imaging with Deep Learning, in PMLR 121:881-891

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