{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:48:04Z","timestamp":1726850884359},"reference-count":26,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,6,10]]},"abstract":"In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.<\/jats:p>","DOI":"10.1155\/2021\/6625688","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T22:23:10Z","timestamp":1623363790000},"page":"1-10","source":"Crossref","is-referenced-by-count":50,"title":["R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2305-5293","authenticated-orcid":true,"given":"Qiang","family":"Zuo","sequence":"first","affiliation":[{"name":"Electronic Engineering College, Heilongjiang University, Harbin 150000, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4365-1850","authenticated-orcid":true,"given":"Songyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Electronic Engineering College, Heilongjiang University, Harbin 150000, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7169-9347","authenticated-orcid":true,"given":"Zhifang","family":"Wang","sequence":"additional","affiliation":[{"name":"Electronic Engineering College, Heilongjiang University, Harbin 150000, China"}]}],"member":"98","reference":[{"key":"1","first-page":"2852","article-title":"Deep neural networks segment neuronal membranes in electron microscopy images","volume":"25","author":"D. 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