Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

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

Yuliang Gu
  • Fonction : Auteur
  • PersonId : 1428559
Zhichao Sun
  • Fonction : Auteur
  • PersonId : 1428560
Jinchi Zhu
  • Fonction : Auteur
  • PersonId : 1428561
Yongchao Xu
  • Fonction : Auteur
  • PersonId : 1428562

Résumé

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.
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Dates et versions

hal-04739628 , version 1 (17-10-2024)

Identifiants

  • HAL Id : hal-04739628 , version 1

Citer

Yuliang Gu, Yepeng Liu, Zhichao Sun, Jinchi Zhu, Yongchao Xu, et al.. Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec 2024, Lisbon (Portugal), Portugal. ⟨hal-04739628⟩
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