Work

Corrigendum to “Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline” [Comput. Biol. Med. 107 (2019) 109–117]

Year: 2021

Type: erratum

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2020.103684

Authors: Hyeonsoo Moon, Yuankai Huo, Richard G. Abramson, Richard Alan Peters, Albert Assad, Tamara K. Moyo, Michael R. Savona, Bennett A. Landman

Institutions: Vanderbilt University, Vanderbilt-Ingram Cancer Center, Incyte (United States), Vanderbilt University Medical Center

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Science Foundation, Intel Corporation, Vanderbilt University, National Institutes of Health, National Institute on Aging, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, National Center for Advancing Translational Sciences

Awards: CAREER: Modeling Personalized Brain Development with Big Data, G2665080673, G2807378203, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI, The Vanderbilt Institute for Clinical and Translational Research (VICTR), Cancer Center Support Grant

OpenAlex ID: W4200548550

Cites:

Cited by: 1

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