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However, the poor quality of CBCT images poses challenges in clinical practice, making annotation difficult due to factors such as image noise, low contrast, and organ\u00a0deformation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The objective of this study is to create a segmentation model for the label\u2010free target domain (CBCT), leveraging valuable insights derived from the label\u2010rich source domain (CT). This goal is achieved by addressing the domain gap across diverse domains through the implementation of a cross\u2010modality medical image segmentation\u00a0framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our approach introduces a multi\u2010scale domain adaptive segmentation method, performing domain adaptation simultaneously at both the image and feature levels. The primary innovation lies in a novel multi\u2010scale anatomical regularization approach, which (i) aligns the target domain feature space with the source domain feature space at multiple spatial scales simultaneously, and (ii) exchanges information across different scales to fuse knowledge from multi\u2010scale\u00a0perspectives.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Quantitative and qualitative experiments were conducted on pelvic CBCT segmentation tasks. The training dataset comprises 40 unpaired CBCT\u2010CT images with only CT images annotated. The validation and testing datasets consist of 5 and 10 CT images, respectively, all with annotations. The experimental results demonstrate the superior performance of our method compared to other state\u2010of\u2010the\u2010art cross\u2010modality medical image segmentation methods. The Dice similarity coefficients (DSC) for CBCT image segmentation results is  %, and the average symmetric surface distance (ASSD) is . Statistical analysis confirms the statistical significance of the improvements achieved by our\u00a0method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our method exhibits superiority in pelvic CBCT image segmentation compared to its counterparts.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17378","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T14:04:52Z","timestamp":1725372292000},"page":"8804-8813","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi\u2010scale anatomical regularization for domain\u2010adaptive segmentation of pelvic CBCT images"],"prefix":"10.1002","volume":"51","author":[{"given":"Xu","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology Huaqiao University Xiamen Fujian China"},{"name":"Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Fujian Province University Xiamen Fujian China"},{"name":"Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen Fujian China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunkui","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of North Carolina Chapel Hill North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pew\u2010Thian","family":"Yap","sequence":"additional","affiliation":[{"name":"Department of Radiology University of North Carolina Chapel Hill North Carolina USA"},{"name":"Biomedical Research Imaging Center University of North Carolina Chapel Hill North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Lian","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of North Carolina Chapel Hill North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14196"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"H\u00e4nschA DickenV KleinJ MorgasT HaasB HahnHK.Artifact\u2010driven sampling schemes for robust female pelvis CBCT segmentation using deep learning. 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