{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:41:23Z","timestamp":1773247283173,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,26]],"date-time":"2020-07-26T00:00:00Z","timestamp":1595721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Arti\ufb01cial Intelligence Program of Shanghai","award":["2019-RGZN-01077"],"award-info":[{"award-number":["2019-RGZN-01077"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFF0101400"],"award-info":[{"award-number":["2016YFF0101400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder\u2013 decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net\u2019s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34% of U-Net (7.76 M), 29% of UNet++ (9.04 M), and 9.1% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc\/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.<\/jats:p>","DOI":"10.3390\/sym12081230","type":"journal-article","created":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T09:24:49Z","timestamp":1595841889000},"page":"1230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-5423","authenticated-orcid":false,"given":"Xiaofei","family":"Qin","sequence":"first","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"},{"name":"Shanghai Key Laboratory of Contemporary Optics System, Shanghai 200093, China"},{"name":"Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education, Shanghai 200093, China"}]},{"given":"Chengzi","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"given":"Hang","family":"Chang","sequence":"additional","affiliation":[{"name":"Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"}]},{"given":"Hao","family":"Lu","sequence":"additional","affiliation":[{"name":"Guangxi Yuchai Machinery Co., Ltd., Nanning, Guangxi 530007, China"}]},{"given":"Xuedian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"},{"name":"Shanghai Key Laboratory of Contemporary Optics System, Shanghai 200093, China"},{"name":"Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education, Shanghai 200093, China"},{"name":"Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. 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