{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T01:30:49Z","timestamp":1773970249027,"version":"3.50.1"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients\u2019 condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin\u2019s Concordance Correlation Coefficient (Lin\u2019s CCC) to evaluate the performance of the algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66\u2009\u00b1\u20090.17. Notably, performance was superior for PH cases (mean DSC of 0.73\u2009\u00b1\u20090.14) compared to HI2 cases (mean DSC of 0.61\u2009\u00b1\u20090.18). Lin\u2019s CCC was 0.88 (95% CI 0.79\u20130.93), indicating a strong agreement between the algorithm\u2019s results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7\u2009s for each patient case.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fninf.2024.1382630","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T05:01:44Z","timestamp":1713243704000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke"],"prefix":"10.3389","volume":"18","author":[{"given":"Jiacheng","family":"Sun","sequence":"first","affiliation":[]},{"given":"Freda","family":"Werdiger","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Blair","sequence":"additional","affiliation":[]},{"given":"Chushuang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Bivard","sequence":"additional","affiliation":[]},{"given":"Longting","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Parsons","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"101908","DOI":"10.1016\/j.compmedimag.2021.101908","article-title":"Hemorrhagic stroke lesion segmentation using a 3D U-net with squeeze-and-excitation blocks","volume":"90","author":"Abramova","year":"2021","journal-title":"Comput. 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