Zhang, X., Ali, S. orcid.org/0000-0003-1313-3542, Liu, T. et al. (9 more authors) (2024) Robust and smooth Couinaud segmentation via anatomical structure-guided point-voxel network. Computers in Biology and Medicine, 182. 109202. ISSN 0010-4825
Abstract
Precise Couinaud segmentation from preoperative liver computed tomography (CT) is crucial for surgical planning and lesion examination. However, this task is challenging as it is defined based on vessel structures, and there is no intensity contrast between adjacent Couinaud segments in CT images. To solve this challenge, we design a multi-scale point-voxel fusion framework, which can more effectively model the spatial relationship of points and the semantic information of the image, producing robust and smooth Couinaud segmentations. Specifically, we first segment the liver and vessels from the CT image and generate 3D liver point clouds and voxel grids embedded with the vessel structure. Then, our method with two input-specific branches extracts complementary feature representations from points and voxels, respectively. The local attention module adaptively fuses features from the two branches at different scales to balance the contribution of different branches in learning more discriminative features. Furthermore, we propose a novel distance loss at the feature level to make the features in the segment more compact, thereby improving the certainty of segmentation between segments. Our experimental results on three public liver datasets demonstrate that our proposed method outperforms several state-of-the-art methods by large margins. Specifically, in out-of-distribution (OOD) testing of LiTS dataset, our method exceeded the voxel-based 3D UNet by approximately 20% in Dice score, and outperformed the point-based PointNet2Plus by approximately 8% in Dice score. Our code and manual annotations of the public datasets presented in this paper are available online: https://github.com/xukun-zhang/Couinaud-Segmentation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is an author produced version of an article published in Computers in Biology and Medicine made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0) in accordance with the publisher's self-archiving policy. |
Keywords: | Couinaud segmentation; Point-voxel network; Liver CT |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Oct 2024 11:35 |
Last Modified: | 07 Oct 2024 11:35 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.compbiomed.2024.109202 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:217972 |
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Filename: CouinaudSegmentation_CIBM.pdf
Licence: CC-BY-NC-ND 4.0
