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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge
Adam E Flanders1, Luciano M Prevedello1, George Shih1
1Department of Radiology/Division of Neuroradiology, Thomas Jefferson University Hospital, 132 S Tenth St, Suite 1080B Main Building, Philadelphia, PA 19107 (A.E.F.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Radiology, Stanford University, Stanford, Calif (S.S.H.); Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Mass (J.K.); Quantitative Sciences Unit, Stanford University, Stanford, Calif (R.B.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); MD.ai, New York, NY (A.S.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.C.K.); Department of Radiology, Stanford University, Stanford, Calif (M.P.L.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (G.C.); Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia (L. Cala); Advanced Diagnostic Imaging, Clínica DAPI, Curitiba, Brazil (L. Coelho); Department of Radiology, University of Washington, Seattle, Wash (M.M.); Department of Radiology, Baylor College of Medicine, Houston, Tex (F.M., C.L.); Department of Radiology, University of Ottawa, Ottawa, Canada (E.M.); Department of Radiology & Biomedical Imaging, Yale University, New Haven, Conn (I.I., V.Z.); Department of Medical Imaging, Gold Coast University Hospital, Southport, Australia (O.M.); Department of Neuroradiology, University of Utah Health Sciences Center, Salt Lake City, Utah (L.S.); Department of Radiology and Medical Imaging, University of Virginia Health, Charlottesville, Va (D.J.); Division of Neuroradiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.A.); Department of Radiology, Albert Einstein Healthcare Network, Philadelphia, Pa (R.K.L.); and Department of Radiology, SUNY Downstate Medical Center, Albany, NY (J.N.).
This dataset provides annotations for five common brain hemorrhage subtypes found on CT scans. These include subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage for improved diagnostics.
Area of Science:
- Neuroimaging
- Radiology
- Medical Informatics
Background:
- Brain CT is crucial for diagnosing intracranial hemorrhages.
- Accurate identification of hemorrhage subtypes is essential for patient management.
- A standardized dataset aids in developing and validating automated detection tools.
Purpose of the Study:
- To present a comprehensive dataset annotating five primary brain hemorrhage subtypes.
- To facilitate research in automated hemorrhage detection and classification using CT imaging.
Main Methods:
- Dataset compilation involving expert annotations.
- Inclusion of five distinct hemorrhage classifications: subarachnoid, intraventricular, subdural, epidural, and intraparenchymal.
- Focus on annotations typically encountered in clinical brain CT scans.
Main Results:
- A curated dataset of brain CT scans with detailed hemorrhage subtype annotations.
- The dataset covers the most frequent types of intracranial bleeding.
Conclusions:
- This annotated dataset serves as a valuable resource for AI development in neuroradiology.
- It supports the advancement of objective and reproducible methods for hemorrhage diagnosis.

