{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T08:53:56Z","timestamp":1770281636862,"version":"3.49.0"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T00:00:00Z","timestamp":1640044800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Planning Project of Xiamen City","award":["3502Z20184036"],"award-info":[{"award-number":["3502Z20184036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,12,21]]},"abstract":"<jats:p>In this paper, we proposed an improved 2D U-Net model integrated squeeze-and-excitation layer for prostate cancer segmentation. The proposed model combined a more complex 2D U-Net model and squeeze-and-excitation technique. The model consisted of an encoder stage and a decoder stage. The encoder stage aims to extract features of the input, which contains CONV blocks, SE layers, and max-pooling layers for improving the feature extraction capability of the model. The decoder aims to map the extracted features to the original image with CONV blocks, SE layers, and upsampling layers. The SE layer is implemented to learn more global and local features. Experiments on the public dataset PROMISE12 have demonstrated that the proposed model could achieve state-of-the-art segmentation performance compared with other traditional methods.<\/jats:p>","DOI":"10.1155\/2021\/8666693","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T21:20:15Z","timestamp":1640121615000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["An Improved 2D U-Net Model Integrated Squeeze-and-Excitation Layer for Prostate Cancer Segmentation"],"prefix":"10.1155","volume":"2021","author":[{"given":"Bingshuai","family":"Liu","sequence":"first","affiliation":[{"name":"School of Informatics Xiamen University, Xiamen University, Xiamen 361000, Fujian, China"}]},{"given":"Jiawei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Informatics Xiamen University, Xiamen University, Xiamen 361000, Fujian, China"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Informatics Xiamen University, Xiamen University, Xiamen 361000, Fujian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2010-845X","authenticated-orcid":true,"given":"Peijie","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhongshan Hospital Affiliated of Xiamen University, Xiamen 361004, Fujian, China"}]},{"given":"Shipeng","family":"Li","sequence":"additional","affiliation":[{"name":"The Third Clinical Medical College of Fujian Medicial University, FuZhou 350122, Fujian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1597-0646","authenticated-orcid":true,"given":"Yuexian","family":"Wen","sequence":"additional","affiliation":[{"name":"Zhongshan Hospital Affiliated of Xiamen University, Xiamen 361004, Fujian, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2009.68"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/app10072601"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2211377"},{"issue":"4","key":"4","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1118\/1.3315367","article-title":"Automated segmentation of the prostate in 3-D MR images using a probabilistic atlas and a spatially constrained deformable model","volume":"37","author":"S. Martin","year":"2010","journal-title":"Medical Physics"},{"key":"5","article-title":"A Deep Learning-Based Method for Prostate Segmentation in T2-Weighted Magnetic Reso-Nance Imaging","author":"D. Karimi","year":"2019"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2508280"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2014.6944225"},{"key":"8","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"J. Long"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10","article-title":"U-Net++ A Nested U-Net Architecture for Medical Image Segmentation","author":"Z. Zhou"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939389"},{"key":"13","article-title":"Deep sparse rectifier neural networks","author":"X. Glorot"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1097\/00004647-200110000-00001"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"16","unstructured":"YeghiazaryanV.VoiculescuI.An overview of current evaluation methods used in medical image segmentation2015Oxford, UKDepartment of Computer ScienceTechnical Report RR-15-08"},{"key":"17","article-title":"Adam A method for stochastic optimization","author":"D. P. Kingma","year":"2014"},{"key":"18","volume-title":"TensorFlow Machine Learning Projects: Build 13 Real-World Projects with Advanced Numerical Computations Using the Python Ecosystem","author":"A. Jain","year":"2018"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2015.08.045"}],"container-title":["Scientific Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/8666693.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/8666693.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/8666693.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T21:20:20Z","timestamp":1640121620000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/sp\/2021\/8666693\/"}},"subtitle":[],"editor":[{"given":"Tongguang","family":"Ni","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,12,21]]},"references-count":19,"alternative-id":["8666693","8666693"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8666693","relation":{},"ISSN":["1875-919X","1058-9244"],"issn-type":[{"value":"1875-919X","type":"electronic"},{"value":"1058-9244","type":"print"}],"subject":[],"published":{"date-parts":[[2021,12,21]]}}}