{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:20:25Z","timestamp":1760710825756,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractable, and the performance of current models is mostly unsatisfactory with indistinguishable and blurred boundaries of segmentation results of tiny tumor volume. To address the problem, we propose a densely connected deep convolutional network consisting of an encoder network and a corresponding decoder network, which extracts high-level semantic features from different levels and uses low-level spatial features concurrently to obtain fine-grained segmented masks. Skip-connection architecture is involved and modified to propagate spatial information to the decoder network. Preliminary experiments are conducted on 30 patients. Experimental results show our model outperforms all baseline models, with improvements of 4.17%. An ablation study is performed, and the effectiveness of the novel loss function is validated.<\/jats:p>","DOI":"10.3390\/s21237877","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["DCNet: Densely Connected Deep Convolutional Encoder\u2013Decoder Network for Nasopharyngeal Carcinoma Segmentation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8203-8293","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9043-722X","authenticated-orcid":false,"given":"Guanghui","family":"Han","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China"},{"name":"School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6198-2453","authenticated-orcid":false,"given":"Xiujian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/S0140-6736(19)30956-0","article-title":"Nasopharyngeal Carcinoma","volume":"394","author":"Chen","year":"2019","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s40880-016-0167-2","article-title":"Prognostic Factors and Failure Patterns in Non-metastatic Nasopharyngeal Carcinoma after Intensity-modulated Radiotherapy","volume":"35","author":"Mao","year":"2016","journal-title":"Chin. 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