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Bayesian VoxDRN: a probabilistic deep voxelwise dilated residual network for whole heart segmentation from 3D MR images

Bayesian VoxDRN: a probabilistic deep voxelwise dilated residual network for whole heart segmentation from 3D MR images
Bayesian VoxDRN: a probabilistic deep voxelwise dilated residual network for whole heart segmentation from 3D MR images

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.

0302-9743
569-577
Springer
Shi, Zenglin
81b5baf7-d948-4c9b-97fc-81168097c5ea
Zeng, Guodong
dacd11b5-8525-4541-8b64-f808f9e1067a
Zhang, Le
1dc1ba6f-92bc-46cc-a73f-a16f21fb8e54
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Yang, Guang
19f7479e-304e-40df-9504-bd3770ea3adf
Zheng, Guoyan
dd195ed1-a1f8-418c-886b-59c083537b97
Frangi, Alejandro F.
Fichtinger, Gabor
Schnabel, Julia A.
Alberola-López, Carlos
Davatzikos, Christos
Shi, Zenglin
81b5baf7-d948-4c9b-97fc-81168097c5ea
Zeng, Guodong
dacd11b5-8525-4541-8b64-f808f9e1067a
Zhang, Le
1dc1ba6f-92bc-46cc-a73f-a16f21fb8e54
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Yang, Guang
19f7479e-304e-40df-9504-bd3770ea3adf
Zheng, Guoyan
dd195ed1-a1f8-418c-886b-59c083537b97
Frangi, Alejandro F.
Fichtinger, Gabor
Schnabel, Julia A.
Alberola-López, Carlos
Davatzikos, Christos

Shi, Zenglin, Zeng, Guodong, Zhang, Le, Zhuang, Xiahai, Li, Lei, Yang, Guang and Zheng, Guoyan (2018) Bayesian VoxDRN: a probabilistic deep voxelwise dilated residual network for whole heart segmentation from 3D MR images. Frangi, Alejandro F., Fichtinger, Gabor, Schnabel, Julia A., Alberola-López, Carlos and Davatzikos, Christos (eds.) In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. vol. 11073 LNCS, Springer. pp. 569-577 . (doi:10.1007/978-3-030-00937-3_65).

Record type: Conference or Workshop Item (Paper)

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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More information

Published date: 13 September 2018
Additional Information: Publisher Copyright: © 2018, Springer Nature Switzerland AG.
Venue - Dates: 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, , Granada, Spain, 2018-09-16 - 2018-09-20

Identifiers

Local EPrints ID: 488644
URI: http://eprints.soton.ac.uk/id/eprint/488644
ISSN: 0302-9743
PURE UUID: d75e07bb-ad59-45fe-890a-4636534167e1
ORCID for Lei Li: ORCID iD orcid.org/0000-0003-1281-6472

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Date deposited: 27 Mar 2024 18:02
Last modified: 06 Jun 2024 02:20

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Contributors

Author: Zenglin Shi
Author: Guodong Zeng
Author: Le Zhang
Author: Xiahai Zhuang
Author: Lei Li ORCID iD
Author: Guang Yang
Author: Guoyan Zheng
Editor: Alejandro F. Frangi
Editor: Gabor Fichtinger
Editor: Julia A. Schnabel
Editor: Carlos Alberola-López
Editor: Christos Davatzikos

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