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Joint supervoxel classification forest for weakly-supervised organ segmentation

Kanavati F, Misawa K, Fujiwara M, Mori K, Rueckert D, Glocker B (2017)

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Publication Type: Conference contribution

Publication year: 2017

Journal

Publisher: Springer Verlag

Book Volume: 10541 LNCS

Pages Range: 79-87

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319673882

DOI: 10.1007/978-3-319-67389-9_10

Abstract

This article presents an efficient method for weakly-supervised organ segmentation. It consists in over-segmenting the images into object-like supervoxels. A single joint forest classifier is then trained on all the images, where (a) the supervoxel indices are used as labels for the voxels, (b) a joint node optimisation is done using training samples from all the images, and (c) in each leaf node, a distinct posterior distribution is stored per image. The result is a forest with a shared structure that efficiently encodes all the images in the dataset. The forest can be applied once on a given source image to obtain supervoxel label predictions for its voxels from all the other target images in the dataset by simply looking up the target’s distribution in the leaf nodes. The output is then regularised using majority voting within the boundaries of the source’s supervoxels. This yields sparse correspondences on an over-segmentation-based level in an unsupervised, efficient, and robust manner. Weak annotations can then be propagated to other images, extending the labelled set and allowing an organ label classification forest to be trained. We demonstrate the effectiveness of our approach on a dataset of 150 abdominal CT images where, starting from a small set of 10 images with scribbles, we perform weakly-supervised image segmentation of the kidneys, liver and spleen. Promising results are obtained.

Involved external institutions

How to cite

APA:

Kanavati, F., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D., & Glocker, B. (2017). Joint supervoxel classification forest for weakly-supervised organ segmentation. In Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 79-87). Quebec City, QC, CAN: Springer Verlag.

MLA:

Kanavati, Fahdi, et al. "Joint supervoxel classification forest for weakly-supervised organ segmentation." Proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang, Springer Verlag, 2017. 79-87.

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