Name
Title | ||
|---|---|---|
Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00937-3_65 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Voxel,Computer vision,Residual,Monte Carlo method,Pattern recognition,Computer science,Segmentation,Posterior probability,Artificial intelligence,Probabilistic logic,Dice,Bayesian probability | Conference | 11073 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.40 |
References | Authors | |
15 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zenglin Shi | 1 | 4 | 0.40 |
| Guodong Zeng | 2 | 27 | 5.28 |
| Le Zhang | 3 | 268 | 32.16 |
| Xiahai Zhuang | 4 | 411 | 38.76 |
| Lei Li | 5 | 33 | 5.37 |
| Guang Yang | 6 | 15 | 5.73 |
| Guoyan Zheng | 7 | 363 | 56.10 |