Dergi makalesi Açık Erişim
Alis, Deniz; Yergin, Mert; Alis, Ceren; Topel, Cagdas; Asmakutlu, Ozan; Bagcilar, Omer; Senli, Yeseren Deniz; Ustundag, Ahmet; Salt, Vefa; Dogan, Sebahat Nacar; Velioglu, Murat; Selcuk, Hakan Hatem; Kara, Batuhan; Oksuz, Ilkay; Kizilkilic, Osman; Karaarslan, Ercan
{ "conceptrecid": "235847", "created": "2022-10-07T09:30:23.530488+00:00", "doi": "10.1038/s41598-021-91467-x", "files": [ { "bucket": "42b6e92e-9aa9-4ac3-bcd2-aafa975fb9f0", "checksum": "md5:7c272da89c28a153cdc5790e988cb97e", "key": "bib-8326fc8e-93bb-4f53-af51-4a1e138bb756.txt", "links": { "self": "https://aperta.ulakbim.gov.tr/api/files/42b6e92e-9aa9-4ac3-bcd2-aafa975fb9f0/bib-8326fc8e-93bb-4f53-af51-4a1e138bb756.txt" }, "size": 362, "type": "txt" } ], "id": 235848, "links": { "badge": "https://aperta.ulakbim.gov.tr/badge/doi/10.1038/s41598-021-91467-x.svg", "bucket": "https://aperta.ulakbim.gov.tr/api/files/42b6e92e-9aa9-4ac3-bcd2-aafa975fb9f0", "doi": "https://doi.org/10.1038/s41598-021-91467-x", "html": "https://aperta.ulakbim.gov.tr/record/235848", "latest": "https://aperta.ulakbim.gov.tr/api/records/235848", "latest_html": "https://aperta.ulakbim.gov.tr/record/235848" }, "metadata": { "access_right": "open", "access_right_category": "success", "communities": [ { "id": "tubitak-destekli-proje-yayinlari" } ], "creators": [ { "affiliation": "Acibadem Mehmet Ali Aydinlar Univ, Dept Radiol, Sch Med, Istanbul, Turkey", "name": "Alis, Deniz" }, { "affiliation": "Bahcesehir Univ, Dept Software Engn & Appl Sci, Istanbul, Turkey", "name": "Yergin, Mert" }, { "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Neurol Dept, Istanbul, Turkey", "name": "Alis, Ceren" }, { "affiliation": "Istanbul Mehmet Akif Ersoy Thorac & Cardiovasc Su, Dept Radiol, Halkali Istanbul, Turkey", "name": "Topel, Cagdas" }, { "affiliation": "Istanbul Mehmet Akif Ersoy Thorac & Cardiovasc Su, Dept Radiol, Halkali Istanbul, Turkey", "name": "Asmakutlu, Ozan" }, { "affiliation": "Istanbul Silivri State Hosp, Radiol Dept, Istanbul, Turkey", "name": "Bagcilar, Omer" }, { "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey", "name": "Senli, Yeseren Deniz" }, { "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey", "name": "Ustundag, Ahmet" }, { "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey", "name": "Salt, Vefa" }, { "affiliation": "Istanbul Gaziosmanpasa Training & Res Hosp, Radiol Dept, Istanbul, Turkey", "name": "Dogan, Sebahat Nacar" }, { "affiliation": "Istanbul Fatih Sultan Mehmet Training & Res Hosp, Radiol Dept, Istanbul, Turkey", "name": "Velioglu, Murat" }, { "affiliation": "Istanbul Bakirkoy Sadi Konuk Training & Res Hosp, Radiol Dept, Istanbul, Turkey", "name": "Selcuk, Hakan Hatem" }, { "affiliation": "Istanbul Bakirkoy Sadi Konuk Training & Res Hosp, Radiol Dept, Istanbul, Turkey", "name": "Kara, Batuhan" }, { "affiliation": "Istanbul Tech Univ, Dept Software Engn & Appl Sci, Istanbul, Turkey", "name": "Oksuz, Ilkay" }, { "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkey", "name": "Kizilkilic, Osman" }, { "affiliation": "Acibadem Mehmet Ali Aydinlar Univ, Dept Radiol, Sch Med, Istanbul, Turkey", "name": "Karaarslan, Ercan" } ], "description": "There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n=2986) and B (n=3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.", "doi": "10.1038/s41598-021-91467-x", "has_grant": false, "journal": { "issue": "1", "title": "SCIENTIFIC REPORTS", "volume": "11" }, "license": { "id": "cc-by" }, "publication_date": "2021-01-01", "relations": { "version": [ { "count": 1, "index": 0, "is_last": true, "last_child": { "pid_type": "recid", "pid_value": "235848" }, "parent": { "pid_type": "recid", "pid_value": "235847" } } ] }, "resource_type": { "subtype": "article", "title": "Dergi makalesi", "type": "publication" }, "science_branches": [ "Di\u011fer" ], "title": "Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study" }, "owners": [ 1 ], "revision": 1, "stats": { "downloads": 7.0, "unique_downloads": 7.0, "unique_views": 20.0, "version_downloads": 7.0, "version_unique_downloads": 7.0, "version_unique_views": 20.0, "version_views": 20.0, "version_volume": 2534.0, "views": 20.0, "volume": 2534.0 }, "updated": "2022-10-07T09:30:23.587542+00:00" }
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