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The low contrast and highly flexible anatomical structure make it challenging to accurately segment the organs or lesions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To improve the segmentation accuracy of the organs or lesions in magnetic resonance (MR) images, which can be useful in clinical diagnosis and treatment of\u00a0cancers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>First, a selective feature interaction (SFI) module is designed to selectively extract the similar features of the sequence images based on the similarity interaction. Second, a multi\u2010scale guided feature reconstruction (MGFR) module is designed to reconstruct low\u2010level semantic features and focus on small targets and the edges of the pancreas. Third, to reduce manual annotation of large amounts of data, a semi\u2010supervised training method is also proposed. Uncertainty estimation is used to further improve the segmentation accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Three hundred ninety\u2010five 3D MR images from 395 patients with pancreatic cancer, 259 3D MR images from 259 patients with brain tumors, and four\u2010fold cross\u2010validation strategy are used to evaluate the proposed method. Compared to state\u2010of\u2010the\u2010art deep learning segmentation networks, the proposed method can achieve better segmentation of pancreas or tumors in MR images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>SFI\u2010Net can fuse dual sequence MR images for abnormal pancreas or tumor segmentation. The proposed semi\u2010supervised strategy can further improve the performance of\u00a0SFI\u2010Net.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16072","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T05:11:22Z","timestamp":1667884282000},"page":"1586-1600","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi\u2010supervised interactive fusion network for MR image segmentation"],"prefix":"10.1002","volume":"50","author":[{"given":"Wenxuan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering Soochow University Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Bian","sequence":"additional","affiliation":[{"name":"Department of Radiology Changhai Hospital The Navy Military Medical University Shanghai 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