Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation

• Main Authors: Gu, Yuliang, Liu, Yepeng, Sun, Zhichao, Zhu, Jinchi, Xu, Yongchao, Najman, Laurent
• Format: Journal Article
• Language: English
• Published: 18-10-2024
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2410.14210

2024 IEEE International Conference on Bioinformatics and
Biomedicine (BIBM), Dec 2024, Lisbon (Portugal), Portugal Annotating 3D medical images demands expert knowledge and is time-consuming.
As a result, semi-supervised learning (SSL) approaches have gained significant
interest in 3D medical image segmentation. The significant size differences
among various organs in the human body lead to imbalanced class distribution,
which is a major challenge in the real-world application of these SSL
approaches. To address this issue, we develop a novel Shape Transformation
driven by Active Contour (STAC), that enlarges smaller organs to alleviate
imbalanced class distribution across different organs. Inspired by curve
evolution theory in active contour methods, STAC employs a signed distance
function (SDF) as the level set function, to implicitly represent the shape of
organs, and deforms voxels in the direction of the steepest descent of SDF
(i.e., the normal vector). To ensure that the voxels far from expansion organs
remain unchanged, we design an SDF-based weight function to control the degree
of deformation for each voxel. We then use STAC as a data-augmentation process
during the training stage. Experimental results on two benchmark datasets
demonstrate that the proposed method significantly outperforms some
state-of-the-art methods. Source code is publicly available at
https://github.com/GuGuLL123/STAC.