Результаты исследований: Материалы конференций › тезисы › Рецензирование
The Siberian multimodal brain tumor image segmentation dataset. / Голушко, Сергей Кузьмич; Амелина, Евгения Валерьевна; Groza, Vladimir и др.
2020. Аннотация от Bioinformatics of Genome Regulation and Structure Systems Biology (BGRS/SB-2020): The Twelfth International Multiconference (06-10 July 2020, Novosibirsk, Russia), Новосибирск, Российская Федерация.Результаты исследований: Материалы конференций › тезисы › Рецензирование
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TY - CONF
T1 - The Siberian multimodal brain tumor image segmentation dataset
AU - Голушко, Сергей Кузьмич
AU - Амелина, Евгения Валерьевна
AU - Groza, Vladimir
AU - Амелин, Михаил Евгеньевич
AU - Толстокулаков, Николай Юрьевич
AU - Тучинов, Баир Николаевич
AU - Pavlovskiy, Evgeny
N1 - Conference code: 12
PY - 2020
Y1 - 2020
N2 - Automatic brain tumor segmentation from CT or MRI scans is one of the crucial problems among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying patients with various pathologies. In this paper, we report the results of the research project ”Brain Tumor Segmentation” organized in conjunction with the Federal Neurosurgical Center. Several state-of-the-art tumor segmentation algorithms were applied to a set of 100 MRI scans of meningioma, neurinoma and glioma patients - manually annotated by up to three raters - and to 100 comparable scans obtained using the automated tumor multi-region segmentation. Quantitative evaluations revealed a considerable agreement between the human raters in segmenting various tumor subregions (Dice scores in the range 85-90%). We found that different algorithms worked best for different sub-regions, but no single algorithm ranked in the top for all subregions simultaneously
AB - Automatic brain tumor segmentation from CT or MRI scans is one of the crucial problems among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying patients with various pathologies. In this paper, we report the results of the research project ”Brain Tumor Segmentation” organized in conjunction with the Federal Neurosurgical Center. Several state-of-the-art tumor segmentation algorithms were applied to a set of 100 MRI scans of meningioma, neurinoma and glioma patients - manually annotated by up to three raters - and to 100 comparable scans obtained using the automated tumor multi-region segmentation. Quantitative evaluations revealed a considerable agreement between the human raters in segmenting various tumor subregions (Dice scores in the range 85-90%). We found that different algorithms worked best for different sub-regions, but no single algorithm ranked in the top for all subregions simultaneously
UR - https://www.mendeley.com/catalogue/a8a1b747-b527-3621-a969-594376704a98/
UR - https://www.mendeley.com/catalogue/a8a1b747-b527-3621-a969-594376704a98/
U2 - 10.18699/BGRS/SB-2020-269
DO - 10.18699/BGRS/SB-2020-269
M3 - Abstract
T2 - Bioinformatics of Genome Regulation and Structure Systems Biology (BGRS/SB-2020): The Twelfth International Multiconference (06-10 July 2020, Novosibirsk, Russia)
Y2 - 6 July 2020 through 10 July 2020
ER -
ID: 26155308