Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Lohendran Baskaran, Subhi J. Al’Aref, Gabriel Maliakal, Benjamin C. Lee, Zhuoran Xu, Jeong W. Choi, Sang-Eun Lee, Ji Min Sung, Fay Y. Lin, Simon Dunham, Bobak Mosadegh, Yong-Jin Kim, Ilan Gottlieb, Byoung Kwon Lee, Eun Ju Chun, Filippo Cademartiri, Erica Maffei, Hugo Marques, Sanghoon Shin, Jung Hyun Choi, Kavitha Chinnaiyan, Martin Hadamitzky, Edoardo Conte, Daniele Andreini, Gianluca Pontone … & Leslee J. Shaw
Objectives: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. Background: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. Methods: Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA),...

Registration Year

  • 2020
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Resource Types

  • Dataset
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Affiliations

  • Emory University School of Medicine
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  • Centro Cardiologico Monzino
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  • Seoul National University Bundang Hospital
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  • Yonsei University
    1
  • Los Angeles Biomedical Research Institute
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  • Pusan National University Hospital
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  • Cedars-Sinai Medical Center
    1
  • Weill Cornell Medicine
    1
  • William Beaumont Hospital
    1
  • CVPath Institute
    1