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Subsequently, a multi-layer adaptive feature fusion strategy is utilized to effectively leverage the correlation between these tasks, resulting in refined segmentation results. Additionally, the proposed method incorporates a multi-layer feature fusion block, which adaptively selects features pertinent to segmentation. Furthermore, error-prediction consistency is also introduced between coarse and refined segmentation for regularization, leading to high-performance segmentation results. What\u2019s more, we constructed a multimodal esophageal tumor segmentation dataset with 902 patients and validated it on this dataset and two publicly available multimodal brain tumor datasets. The results indicate that our method achieved Dice scores of 89.04% in esophageal tumor segmentation, 77.01% in whole glioma segmentation, and 91.14% in Vestibular Schwannoma segmentation. This performance surpasses that of segmentation using only available modalities and other image synthesis-based segmentation methods, demonstrating the superior robustness of CSS-Net.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad8e2c","type":"journal-article","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T22:54:08Z","timestamp":1730588048000},"page":"045064","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["CSS-Net: a collaborative framework for synthesis and segmentation of missing contrast-enhanced image with error-prediction consistency"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0946-1351","authenticated-orcid":false,"given":"Xiaoyu","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0012-9273","authenticated-orcid":false,"given":"Feixiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5182-5323","authenticated-orcid":false,"given":"Yong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6310-2977","authenticated-orcid":true,"given":"Kai","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"mlstad8e2cbib1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230681","article-title":"Generative adversarial network\u2013based noncontrast CT angiography for aorta and carotid arteries","volume":"309","author":"Lyu","year":"2023","journal-title":"Radiology"},{"key":"mlstad8e2cbib2","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","article-title":"HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation","volume":"38","author":"Dolz","year":"2018","journal-title":"IEEE Trans. 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