A Comparative Analysis of U-Net and Vision Transformer Architectures in Semi-Supervised Prostate Zonal Segmentation

Author:

Huang Guantian1,Xia Bixuan1,Zhuang Haoming1,Yan Bohan1,Wei Cheng2,Qi Shouliang1ORCID,Qian Wei1,He Dianning3

Affiliation:

1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110057, China

2. School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK

3. School of Health Management, China Medical University, No. 77 Puhe Road Shenyang North New Area, Shenyang 110122, China

Abstract

The precise segmentation of different regions of the prostate is crucial in the diagnosis and treatment of prostate-related diseases. However, the scarcity of labeled prostate data poses a challenge for the accurate segmentation of its different regions. We perform the segmentation of different regions of the prostate using U-Net- and Vision Transformer (ViT)-based architectures. We use five semi-supervised learning methods, including entropy minimization, cross pseudo-supervision, mean teacher, uncertainty-aware mean teacher (UAMT), and interpolation consistency training (ICT) to compare the results with the state-of-the-art prostate semi-supervised segmentation network uncertainty-aware temporal self-learning (UATS). The UAMT method improves the prostate segmentation accuracy and provides stable prostate region segmentation results. ICT plays a more stable role in the prostate region segmentation results, which provides strong support for the medical image segmentation task, and demonstrates the robustness of U-Net for medical image segmentation. UATS is still more applicable to the U-Net backbone and has a very significant effect on a positive prediction rate. However, the performance of ViT in combination with semi-supervision still requires further optimization. This comparative analysis applies various semi-supervised learning methods to prostate zonal segmentation. It guides future prostate segmentation developments and offers insights into utilizing limited labeled data in medical imaging.

Funder

National Natural Science Foundation of China

Science and Technology Foundation of Liaoning Provincial

Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

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