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2018 | OriginalPaper | Chapter

Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images

Authors : Zenglin Shi, Guodong Zeng, Le Zhang, Xiahai Zhuang, Lei Li, Guang Yang, Guoyan Zheng

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a probabilistic deep voxelwise dilated residual network, referred as Bayesian VoxDRN, to segment the whole heart from 3D MR images. Bayesian VoxDRN can predict voxelwise class labels with a measure of model uncertainty, which is achieved by a dropout-based Monte Carlo sampling during testing to generate a posterior distribution of the voxel class labels. Our method has three compelling advantages. First, the dropout mechanism encourages the model to learn a distribution of weights with better data-explanation ability and prevents over-fitting. Second, focal loss and Dice loss are well encapsulated into a complementary learning objective to segment both hard and easy classes. Third, an iterative switch training strategy is introduced to alternatively optimize a binary segmentation task and a multi-class segmentation task for a further accuracy improvement. Experiments on the MICCAI 2017 multi-modality whole heart segmentation challenge data corroborate the effectiveness of the proposed method.

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previous chapter The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation

next chapter Real-Time Prediction of Segmentation Quality

One can find details about the MICCAI 2017 MM-WHS challenge at: http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/.

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Title: Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images
Authors: Zenglin Shi
Guodong Zeng
Le Zhang
Xiahai Zhuang
Lei Li
Guang Yang
Guoyan Zheng
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00936-6

Electronic ISBN: 978-3-030-00937-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00937-3_65

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