{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:47:22Z","timestamp":1781869642105,"version":"3.54.5"},"reference-count":21,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Deep learning techniques have been employed for automatic VS tumor segmentation, including 2D, 2.5D, and 3D UNet\u2010like architectures, which is a specific CNN designed to improve automatic segmentation for medical imaging. Specifically, we introduce a sequential connection where the first UNet's predicted segmentation map is passed to a second complementary network for refinement. Additionally, spatial attention mechanisms are utilized to further guide refinement in the second network.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We conducted experiments on both public and private datasets containing contrast\u2010enhanced T1 and high\u2010resolution T2\u2010weighted magnetic resonance imaging (MRI). Across the public dataset, we observed consistent improvements in Dice scores for all variants of 2D, 2.5D, and 3D CNN methods, with a notable enhancement of 8.86% for the 2D UNet variant on T1. In our private dataset, a 3.75% improvement was reported for 2D T1. Moreover, we found that T1 images generally outperformed T2 in VS segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We demonstrate that sequential connection of UNets combined with spatial attention mechanisms enhances VS segmentation performance across state\u2010of\u2010the\u2010art 2D, 2.5D, and 3D deep learning methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3 <jats:italic>Laryngoscope<\/jats:italic>, 135:1301\u20131308, 2025<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.31979","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T09:19:01Z","timestamp":1735809541000},"page":"1301-1308","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks"],"prefix":"10.1002","volume":"135","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6243-111X","authenticated-orcid":false,"given":"Sophia Marie","family":"H\u00e4u\u00dfler","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology University Medical Center Hamburg\u2010Eppendorf  Hamburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian S.","family":"Betz","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology University Medical Center Hamburg\u2010Eppendorf  Hamburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Della Seta","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Charit\u00e9\u2010Universit\u00e4tsmedizin Berlin Berlin Humboldt Universit\u00e4t zu Berlin and Berlin Institute of Health  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis","family":"Eggert","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology University Medical Center Hamburg\u2010Eppendorf  Hamburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Schlaefer","sequence":"additional","affiliation":[{"name":"Institute of Medical Technology and Intelligent Systems Hamburg University of Technology  Hamburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8552-2227","authenticated-orcid":false,"given":"Debayan","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology University Medical Center Hamburg\u2010Eppendorf  Hamburg Germany"},{"name":"Institute of Medical Technology and Intelligent Systems Hamburg University of Technology  Hamburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1159\/000331394"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noz153"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000002110"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.otc.2011.12.008"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.5152\/iao.2023.22922"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13023-023-02911-2"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(09)60259-2"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/00006123-199605000-00004"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A7685"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fradi.2022.837191"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.28695"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11091676"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-023-02263-y"},{"key":"e_1_2_7_15_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019. 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