{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:02:52Z","timestamp":1777705372344,"version":"3.51.4"},"reference-count":14,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet\u2019s skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU.<\/jats:p>","DOI":"10.3233\/jifs-233232","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T11:57:45Z","timestamp":1703851065000},"page":"4077-4092","source":"Crossref","is-referenced-by-count":1,"title":["A Lightweight convolutional medical segmentation algorithm based on ConvNeXt to improve UNet"],"prefix":"10.1177","volume":"46","author":[{"given":"Chuantao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China"},{"name":"Beijing Building Safety Monitoring Engineering Technology Research Center, Beijing, China"}]},{"given":"Xiumin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China"}]},{"given":"Jiliang","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}]},{"given":"Shuo","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-233232_ref1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.7861\/clinmedicine.18-3-219","article-title":"Point-of-care ultrasound (POCUS): unnecessary gadgetry or evidence-based medicine?","volume":"18","author":"Smallwood","year":"2018","journal-title":"Clinical Medicine"},{"issue":"6","key":"10.3233\/JIFS-233232_ref2","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1016\/j.chest.2017.02.003","article-title":"Point-of-Care Ultrasonography for Evaluation of Acute Dyspnea in the ED","volume":"151","author":"Zanobetti","year":"2017","journal-title":"Chest"},{"key":"10.3233\/JIFS-233232_ref3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.resuscitation.2016.09.018","article-title":"Emergency department point-of-care ultrasound in out-of-hospital and in-ED cardiac arrest","volume":"109","author":"Gaspari","year":"2016","journal-title":"Resuscitation"},{"issue":"1","key":"10.3233\/JIFS-233232_ref4","doi-asserted-by":"crossref","first-page":"20461","DOI":"10.1038\/s41598-022-24513-x","article-title":"Novice-performed point-of-care ultrasound for home-based imaging","volume":"12","author":"Duggan","year":"2022","journal-title":"Scientific Reports"},{"issue":"3","key":"10.3233\/JIFS-233232_ref5","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1148\/radiol.211721","article-title":"Point-of-Care Brain MRI: Preliminary Results from a Single-Center Retrospective Study","volume":"305","author":"Kuoy","year":"2022","journal-title":"Radiology"},{"issue":"11","key":"10.3233\/JIFS-233232_ref6","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s11886-020-01394-y","article-title":"Point-of-Care Ultrasound","volume":"22","author":"Lee","year":"2020","journal-title":"Current Cardiology Reports"},{"issue":"6","key":"10.3233\/JIFS-233232_ref8","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"3","key":"10.3233\/JIFS-233232_ref12","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1016\/j.bbe.2020.07.007","article-title":"A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images","volume":"40","author":"Khanna","year":"2020","journal-title":"Biocybernetics and Biomedical Engineering"},{"issue":"1","key":"10.3233\/JIFS-233232_ref18","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A Survey on Visual Transformer","volume":"45","author":"Han","year":"2023","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/JIFS-233232_ref21","first-page":"1140","article-title":"Segnext: Rethinking convolutional attention design for semantic segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.3233\/JIFS-233232_ref30","doi-asserted-by":"crossref","first-page":"106960","DOI":"10.1016\/j.compbiomed.2023.106960","article-title":"BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation","volume":"159","author":"Zhang","year":"2023","journal-title":"Computers in Biology and Medicine"},{"key":"10.3233\/JIFS-233232_ref32","doi-asserted-by":"crossref","first-page":"109512","DOI":"10.1016\/j.knosys.2022.109512","article-title":"ConvUNeXt: An efficient convolution neural network for medical image segmentation","volume":"253","author":"Han","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/JIFS-233232_ref40","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","article-title":"Gland segmentation in colon histology images: The glas challenge contest","volume":"35","author":"Sirinukunwattana","year":"2017","journal-title":"Medical Image Analysis"},{"key":"10.3233\/JIFS-233232_ref41","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Al-Dhabyani","year":"2020","journal-title":"{Data in brief"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-233232","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:39Z","timestamp":1777455819000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-233232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,14]]},"references-count":14,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-233232","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,14]]}}}