Journal article
Block level skip connections across cascaded V-Net for Multi-Organ segmentation
IEEE Transactions on Medical Imaging, Vol.39(9), pp.2782-2793
2020
Abstract
Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections ( BLSC ) across cascaded V-Net. Our model can take full advantage of features from the first stage network and make the cascaded structure more efficient. We also combine stacked small and large kernels with an inception-like structure to help our model to learn more patterns, which produces superior results for multi-organ segmentation. In addition, some small organs are commonly occluded by large organs and have unclear boundaries with other surrounding tissues, which makes them hard to be segmented. We therefore first locate the small organs through a multi-class network and crop them randomly with the surrounding region, then segment them with a single-class network. We evaluated our model on SegTHOR 2019 challenge unseen testing set and Multi-Atlas Labeling Beyond the Cranial Vault challenge validation set. Our model has achieved an average dice score gain of 1.62 percents and 3.90 percents compared to traditional cascaded networks on these two datasets, respectively. For hard-to-segment small organs, such as the esophagus in SegTHOR 2019 challenge, our technique has achieved a gain of 5.63 percents on dice score, and four organs in Multi-Atlas Labeling Beyond the Cranial Vault challenge have achieved a gain of 5.27 percents on average dice score.
Details
- Title
- Block level skip connections across cascaded V-Net for Multi-Organ segmentation
- Authors/Creators
- L. Zhang (Author/Creator) - Xidian UniversityJ. Zhang (Author/Creator) - Xidian UniversityP. Shen (Author/Creator) - Xidian UniversityG. Zhu (Author/Creator) - Xidian UniversityP. Li (Author/Creator) - Shanghai Blood CenterX. Lu (Author/Creator) - Shanghai Blood CenterH. Zhang (Author/Creator) - Ruijin HospitalS.A.A. Shah (Author/Creator) - Murdoch UniversityM. Bennamoun (Author/Creator) - The University of Western Australia
- Publication Details
- IEEE Transactions on Medical Imaging, Vol.39(9), pp.2782-2793
- Publisher
- IEEE
- Identifiers
- 991005543994807891
- Copyright
- © 2020 IEEE
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
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- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
- Web Of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- Radiology, Nuclear Medicine & Medical Imaging
- ESI research areas
- Clinical Medicine