A Semi-Supervised Multi-Region Segmentation Framework of Bladder Wall and Tumor with Wall-Enhanced Self-Supervised Pre-Training

Overview

Journal Bioengineering (Basel)

Date 2025 Jan 8

PMID 39768043

Authors

Jie Wei

Yao Zheng

Dong Huang

Yang Liu

Xiaopan Xu

Hongbing Lu

Affiliations

Soon will be listed here.

Abstract

Bladder cancer is a prevalent and highly recurrent malignancy within the urinary tract. The accurate segmentation of the bladder wall and tumor in magnetic resonance imaging (MRI) is a crucial step in distinguishing between non-muscle-invasive and muscle-invasive types of bladder cancer, which plays a pivotal role in guiding clinical treatment decisions and influencing postoperative quality of life. The performance of data-driven methods is highly dependent on the quality of the annotations and datasets, however the amount of high-quality annotated data is very limited given the difficulty of professional radiologists to distinguish the mixed regions between the bladder wall and the tumor. The performance of the data-driven approach is highly dependent on the quality of the annotation and datasets, Therefore, in order to alleviate these problems and take full advantage of the potential of limited annotated and unlabeled data, we designed a semi-supervised multi-region framework for bladder wall and tumor segmentation. Our framework incorporates wall-enhanced self-supervised pre-training, designed to enhance discrimination of the bladder wall, and a semi-supervised segmentation network that utilizes both limited high-quality annotated data and unlabeled data. Contrast consistency and reconstruction observation losses are introduced to constrain the model to enhance the bladder walls, and adaptive learning rate and post-processing techniques are implemented to further improve segmentation performance. Extensive experimental validation demonstrated that our proposed method achieves promising results in the segmentation of both the bladder wall and the tumor. The average Dice Similarity Coefficients (DSCs) of the proposed method for the bladder wall and tumor were 0.8351 and 0.9175, respectively. Visualization results indicated that our method can effectively reduce excessive segmentation artifacts outside the bladder, and improve the clinical significance of the segmentation results.

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