Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning

• Published in: Diagnostics (Basel) Vol. 14; no. 16; p. 1751
• Main Authors: Yang, Mingjing, Wu, Zhicheng, Zheng, Hanyu, Huang, Liqin, Ding, Wangbin, Pan, Lin, Yin, Lei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.08.2024; MDPI
• Subjects: Adaptation; Automation; cross modality segmentation; cross pseudo supervision; Datasets; Diagnostic imaging; feature alignment; Image segmentation; Knowledge; Learning; Machine learning; Technology application; unsupervised domain adaptation; China; cross pseudo supervision; feature alignment; unsupervised domain adaptation; cross modality segmentation
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14161751

Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.