دورية أكاديمية
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Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

التفاصيل البيبلوغرافية
العنوان: Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.
المؤلفون: Boaro A; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. alessandro.boaro@univr.it.; Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy. alessandro.boaro@univr.it., Kaczmarzyk JR; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.; Medical Scientist Training Program, Stony Brook University School of Medicine, Stony Brook, NY, USA., Kavouridis VK; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Harary M; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA., Mammi M; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Dawood H; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Shea A; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Cho EY; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Juvekar P; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Noh T; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Rana A; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA., Ghosh S; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA. satra@mit.edu., Arnaout O; Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
المصدر: Scientific reports [Sci Rep] 2022 Sep 14; Vol. 12 (1), pp. 15462. Date of Electronic Publication: 2022 Sep 14.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Deep Learning* , Meningeal Neoplasms*/diagnostic imaging , Meningioma*/diagnostic imaging, Brain/diagnostic imaging ; Humans ; Neural Networks, Computer
مستخلص: Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6-91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.
(© 2022. The Author(s).)
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معلومات مُعتمدة: R01EB020740 United States EB NIBIB NIH HHS; T32GM008444 United States GM NIGMS NIH HHS; RF1 MH121885 United States MH NIMH NIH HHS; P41 EB019936 United States EB NIBIB NIH HHS; RF1MH121885 United States MH NIMH NIH HHS
تواريخ الأحداث: Date Created: 20220914 Date Completed: 20220916 Latest Revision: 20230701
رمز التحديث: 20240829
مُعرف محوري في PubMed: PMC9474556
DOI: 10.1038/s41598-022-19356-5
PMID: 36104424
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-022-19356-5