{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T17:02:03Z","timestamp":1789837323235,"version":"4.0.1"},"reference-count":26,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Uterine fibroid is the most common benign tumor in female reproductive organs. In order to guide the treatment, it is crucial to detect the location, shape, and size of the tumor. This study proposed a deep learning approach based on attention mechanisms to segment uterine fibroids automatically on preoperative Magnetic Resonance (MR) images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed method is based on U\u2010Net architecture and integrates two attention mechanisms: channel attention of squeeze\u2010and\u2010excitation (SE) blocks with residual connections, spatial attention of pyramid pooling module (PPM). We did the ablation study to verify the performance of these two attention mechanisms module and compared DARU\u2010Net with other deep learning methods. All experiments were performed on a clinical dataset consisting of 150 cases collected from our hospital. Among them, 120 cases were used as the training set, and 30 cases are used as the test set. After preprocessing and data augmentation, we trained the network and tested it on the test dataset. We evaluated segmentation performance through the Dice similarity coefficient (DSC), precision, recall, and Jaccard index (JI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The average DSC, precision, recall, and JI of DARU\u2010Net reached 0.8066 \u00b1 0.0956, 0.8233 \u00b1 0.1255, 0.7913 \u00b1 0.1304, and 0.6743 \u00b1 0.1317. Compared with U\u2010Net and other deep learning methods, DARU\u2010Net was more accurate and stable.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This work proposed an optimized U\u2010Net with channel and spatial attention mechanisms to segment uterine fibroids on preoperative MR images. Results showed that DARU\u2010Net was able to accurately segment uterine fibroids from MR images.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13937","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T06:27:20Z","timestamp":1680157640000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["DARU\u2010Net: A dual attention residual U\u2010Net for uterine fibroids segmentation on MRI"],"prefix":"10.1002","volume":"24","author":[{"given":"Jian","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Ultrasound in Medicine and Engineering College of Biomedical Engineering Chongqing Medical University  Chongqing China"},{"name":"Chongqing Key Laboratory of Biomedical Engineering Chongqing Medical University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"State 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