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COVID-19 Lesion Segmentation

Semantic segmentation of Ground Glass Opacities (GGO) in CT images of patients diagnosed with COVID-19.

Resources used
MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset
S.P. Morozov, A.E. Andreychenko, N.A. Pavlov, A.V. Vladzymyrskyy, N.V. Ledikhova, V.A. Gombolevskiy, I.A. Blokhin, P.B. Gelezhe, A.V. Gonchar, V.Yu. Chernina

Publications

COVID-19 CT Lung and Infection Segmentation Dataset

Jun M, Cheng G, Yixin W, Xingle A, Jiantao G, Ziqi Y, Minqing Z, Xin L, Xueyuan D, Shucheng C, Hao W, Sen M, Xiaoyu Y, Ziwei N, Chen L, Lu T, Yuntao Z, Qiongjie Z, Guoqiang D, Jian H - Zenodo - 2020

10.5281/zenodo.3757476

IMAGED OBJECTS

human, lung

Ground Glass Opacities (GGO) in red of a with COVID-19 diagnosed patient.

©CC BY-NC-SD 4.0

COVID-19 Lesion Segmentation in Helmholtz Imaging CONNECT:

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Timo Christian

Deutsches Elektronen-Synchrotron - Hamburg

Project Manager for the Helmholtz Transfer Academy "Future Innovators". Interested in anything that can solve problems. Trying to unlock the potential of scientific development to also solve short-term problems of the real-world.

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Karol Gotkowski

Deutsches Krebsforschungszentrum - Heidelberg

Interested in deep interactive learning, active learning, semantic/instance segmentation, object detection, interpretability, image manipulation detection. Working with both 2D and 3D data.

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Fabian Isensee

Deutsches Krebsforschungszentrum - Heidelberg

Interested in everything that is computer vision, regardless of domain

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Computed tomography (CT) is a non-invasive medical imaging technique allowing a detailed internal view of body structures.
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