Radiol Artif Intell 2020 Sep 30;2(5):e190183. Epub 2020 Sep 30.
Departments of Radiology (J.C.C., K.A.P., S.H., P.R., G.M.C., D.C.V., Q.H., B.J.E.) and Cardiovascular Science (Z.A.), Mayo Clinic Rochester, 200 First St. SW, RO_PB_02_RIL, Rochester, MN 55905; Department of Radiology, Khon Kaen University, Khon Kaen, Thailand (A.B.); Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, Fla (A.D.W.); and Department of Internal Medicine, Ascension St. John Hospital, Detroit, Mich (A.Z.).
Purpose: To develop a deep learning model that segments intracranial structures on head CT scans.
Materials And Methods: In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27-95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. Read More