Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior and Contrastive Similarity.
FOS: Computer and information sciences
Computer Science - Machine Learning
03 medical and health sciences
0302 clinical medicine
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
Image Processing, Computer-Assisted
FOS: Electrical engineering, electronic engineering, information engineering
Diagnosis, Computer-Assisted
Electrical Engineering and Systems Science - Image and Video Processing
Artifacts
Machine Learning (cs.LG)
DOI:
10.48550/arxiv.2302.02125
Publication Date:
2023-10-01
AUTHORS (4)
ABSTRACT
Weakly-supervised Segmentation, Medical Image Segmentation, Contrastive Similarity, Geometric Prior, Point Cloud<br/>Medical image segmentation is almost the most important pre-processing procedure in computer-aided diagnosis but is also a very challenging task due to the complex shapes of segments and various artifacts caused by medical imaging, (i.e., low-contrast tissues, and non-homogenous textures). In this paper, we propose a simple yet effective segmentation framework that incorporates the geometric prior and contrastive similarity into the weakly-supervised segmentation framework in a loss-based fashion. The proposed geometric prior built on point cloud provides meticulous geometry to the weakly-supervised segmentation proposal, which serves as better supervision than the inherent property of the bounding-box annotation (i.e., height and width). Furthermore, we propose contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish low-contrast tissues. The proposed contrastive embedding space can make up for the poor representation of the conventionally-used gray space. Extensive experiments are conducted to verify the effectiveness and the robustness of the proposed weakly-supervised segmentation framework. The proposed framework is superior to state-of-the-art weakly-supervised methods on the following publicly accessible datasets: LiTS 2017 Challenge, KiTS 2021 Challenge, and LPBA40. We also dissect our method and evaluate the performance of each component.<br/>
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