Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity

Abstract

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 lossbased 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 the contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish lowcontrast tissues. The proposed contrastive embedding space can make up for the poor representation of the conventionallyused gray space. Extensive experiments are conducted to verify the effectiveness and the robustness of the proposed weaklysupervised segmentation framework. The proposed framework are 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.