{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T14:51:10Z","timestamp":1785768670306,"version":"3.56.0"},"reference-count":38,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Inner ear segmentation supports the diagnosis and treatment planning of auditory\u2010related disorders such as Meniere's disease. However, manual annotation of inner ear structures on MR images is both time\u2010consuming and labor\u2010intensive. To address this limitation, we developed a deep learning method that takes T2\u2010weighted MR images as input and segments the inner ear into three subregions: the cochlear basal turn, the cochlear mid\u2010to\u2010apical turn, and the vestibule including the semicircular canals. A total of 74 three\u2010dimensional (3D) T2\u2010weighted MR images were retrospectively collected and divided into 50 training cases and 24 internal test cases. In addition, eight MR images from the publicly available Vestibular\u2010Schwannoma\u2010SEG dataset were used as an independent external test set. Ground\u2010truth segmentations for both the internal and external datasets were manually annotated by an experienced radiologist. A 3D transformer\u2010based deep learning model was trained using these annotated data. To further improve segmentation performance, particularly for the thin and intricate semicircular canals, a label\u2010preserving data augmentation strategy was introduced and compared with a conventional data augmentation approach. Segmentation performance was evaluated using quantitative and qualitative assessments. Quantitative evaluation included the Dice similarity coefficient (DSC), Intersection over union (IoU), and Hausdorff distance (HD). On the internal test set, the proposed method achieved a mean DSC of 0.905 (vs. 0.904), a mean IoU of 0.828 (vs. 0.824), and a mean HD of 2.75 (vs. 3.04) compared with the conventional augmentation approach. On the external dataset, the proposed method demonstrated larger improvements, achieving a mean DSC of 0.919 (vs. 0.847), a mean IoU of 0.852 (vs. 0.738), and a mean HD of 3.92 (vs. 4.45). These results demonstrate that the proposed label\u2010preserving data augmentation method improves the robustness and accuracy of inner ear subregion segmentation, particularly for thin and intricate structures.<\/jats:p>","DOI":"10.1002\/nbm.70266","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:25:13Z","timestamp":1773390313000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning\u2010Based Inner Ear Subregion Segmentation in 3D T2\u2010Weighted MRI Using Label\u2010Preserving Data Augmentation"],"prefix":"10.1002","volume":"39","author":[{"given":"Wooseung","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Hankuk University of Foreign Studies  Yongin Republic of Korea"},{"name":"Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology  Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeonah","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Radiology Inje University College of Medicine, Haeundae Paik Hospital  Busan Republic of Korea"},{"name":"Seoil Medical Group Clinic  Busan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seokhwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology Inje University College of Medicine, Haeundae Paik Hospital  Busan Republic of Korea"},{"name":"Department of Otorhinolaryngology and Biomedical Research Institute Pusan National University Hospital, Pusan National University School of Medicine  Busan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho\u2010Joon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology Inje University College of Medicine, Haeundae Paik Hospital  Busan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2149-0072","authenticated-orcid":false,"given":"Yoonho","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Hankuk University of Foreign Studies  Yongin Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0033-1354598"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(09)62071-7"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(08)61161-7"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.27793"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-019-09407-9"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5825-0"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A4822"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/00016480902729827"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.2463\/mrms.mp.2015-0121"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-63887-8"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.710422"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-022-07328-4"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/00016489.2020.1827159"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000003930"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-10587-w"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2025.112382"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims-24-196"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-10670-2"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693"},{"key":"e_1_2_9_21_1","unstructured":"C.Todd M.Kirillov M.Tarabichi F.Naghdy andG.Naghdy An Analysis of Medical Image Processing Methods for Segmentation of the Inner Ear.2009."},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-019-09488-6"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.663200"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-45466-9"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-82289-y"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105630"},{"key":"e_1_2_9_27_1","doi-asserted-by":"crossref","unstructured":"D.Xianfen C.Siping L.Changhong andW.Yuanmei 3D Semi\u2010Automatic Segmentation of the Cochlea and Inner Ear. 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