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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\u2019s success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.<\/jats:p>","DOI":"10.3390\/diagnostics14161751","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T08:54:08Z","timestamp":1723452848000},"page":"1751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6068-8287","authenticated-orcid":false,"given":"Mingjing","family":"Yang","sequence":"first","affiliation":[{"name":"College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanyu","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8602-6380","authenticated-orcid":false,"given":"Liqin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wangbin","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Medical Imaging, Fujian Medical University, Fuzhou 350122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5271-6295","authenticated-orcid":false,"given":"Lin","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Yin","sequence":"additional","affiliation":[{"name":"The Departments of Radiology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou 350001, China"},{"name":"Fujian Provincial Hospital, Fuzhou 350001, China"},{"name":"Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"ref_1","first-page":"305","article-title":"Motivational interviewing: A systematic review and meta-analysis","volume":"55","author":"Rubak","year":"2005","journal-title":"Br. 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