Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

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Bibliographic Details
Title: Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.
Authors: Priya S; Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa (S.P.). Electronic address: sarv-priya@uiowa.edu., Dhruba DD; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.)., Perry SS; Department of Biostatistics, University of Iowa, Iowa City, Iowa (S.S.P.)., Aher PY; Department of Radiology, University of Miami, Miller School of Medicine, Miami, Florida (P.Y.A.)., Gupta A; Department of Radiology, University Hospital Cleveland Medical Center, Cleveland, Ohio (A.G.)., Nagpal P; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin (P.N.)., Jacob M; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.).
Source: Academic radiology [Acad Radiol] 2024 Feb; Vol. 31 (2), pp. 503-513. Date of Electronic Publication: 2023 Aug 03.
Publication Type: Journal Article
Journal Info: Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1878-4046
DOI:10.1016/j.acra.2023.07.008