{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:25:42Z","timestamp":1773840342785,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007632","name":"Department of Research, Changhua Christian Hospital","doi-asserted-by":"publisher","award":["111-CCH-IRP-023"],"award-info":[{"award-number":["111-CCH-IRP-023"]}],"id":[{"id":"10.13039\/501100007632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, an advanced semantic segmentation method and deep convolutional neural network was applied to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images, thereby facilitating image interpretation and diagnosis by providing radiologists an objective second opinion. A total of 684 images (380 benign and 308 malignant tumours) from 343 patients (190 benign and 153 malignant breast tumour patients) were analysed in this study. Six malignancy-related standardised BI-RADS features were selected after analysis. The DeepLab v3+ architecture and four decode networks were used, and their semantic segmentation performance was evaluated and compared. Subsequently, DeepLab v3+ with the ResNet-50 decoder showed the best performance in semantic segmentation, with a mean accuracy and mean intersection over union (IU) of 44.04% and 34.92%, respectively. The weighted IU was 84.36%. For the diagnostic performance, the area under the curve was 83.32%. This study aimed to automate identification of the malignant BI-RADS lexicon on breast ultrasound images to facilitate diagnosis and improve its quality. The evaluation showed that DeepLab v3+ with the ResNet-50 decoder was suitable for solving this problem, offering a better balance of performance and computational resource usage than a fully connected network and other decoders.<\/jats:p>","DOI":"10.3390\/s22145352","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:19:21Z","timestamp":1658189961000},"page":"5352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Semantic Segmentation of the Malignant Breast Imaging Reporting and Data System Lexicon on Breast Ultrasound Images by Using DeepLab v3+"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-840X","authenticated-orcid":false,"given":"Wei-Chung","family":"Shia","sequence":"first","affiliation":[{"name":"Molecular Medicine Laboratory, Department of Research, Changhua Christian Hospital, Changhua 500, Taiwan"},{"name":"Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9791-317X","authenticated-orcid":false,"given":"Fang-Rong","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan"}]},{"given":"Seng-Tong","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan"}]},{"given":"Shih-Lin","family":"Guo","sequence":"additional","affiliation":[{"name":"Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua 500, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0897-4374","authenticated-orcid":false,"given":"Dar-Ren","family":"Chen","sequence":"additional","affiliation":[{"name":"Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua 500, Taiwan"},{"name":"School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"ref_1","first-page":"113","article-title":"Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection","volume":"16","author":"Jalalian","year":"2017","journal-title":"EXCLI J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101829","DOI":"10.1016\/j.compmedimag.2020.101829","article-title":"Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine","volume":"87","author":"Shia","year":"2021","journal-title":"Comput. 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