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Multi-modal body part segmentation of infants using deep learning.

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Title: Multi-modal body part segmentation of infants using deep learning.
Authors: Voss F; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland. voss@hia.rwth-aachen.de., Brechmann N; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.; Fraunhofer Institute for Microelectronic Circuits and Systems, Duisburg, Germany., Lyra S; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland., Rixen J; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland., Leonhardt S; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland., Hoog Antink C; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.; KIS*MED (AI Systems in Medicine), Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.
Source: Biomedical engineering online [Biomed Eng Online] 2023 Mar 22; Vol. 22 (1), pp. 28. Date of Electronic Publication: 2023 Mar 22.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101147518 Publication Model: Electronic Cited Medium: Internet ISSN: 1475-925X (Electronic) Linking ISSN: 1475925X NLM ISO Abbreviation: Biomed Eng Online Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Multi-modal body part segmentation of infants using deep learning.
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  Data: <searchLink fieldCode="AU" term="%22Voss+F%22">Voss F</searchLink>; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland. voss@hia.rwth-aachen.de.<br /><searchLink fieldCode="AU" term="%22Brechmann+N%22">Brechmann N</searchLink>; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.; Fraunhofer Institute for Microelectronic Circuits and Systems, Duisburg, Germany.<br /><searchLink fieldCode="AU" term="%22Lyra+S%22">Lyra S</searchLink>; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.<br /><searchLink fieldCode="AU" term="%22Rixen+J%22">Rixen J</searchLink>; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.<br /><searchLink fieldCode="AU" term="%22Leonhardt+S%22">Leonhardt S</searchLink>; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.<br /><searchLink fieldCode="AU" term="%22Hoog+Antink+C%22">Hoog Antink C</searchLink>; Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.; KIS*MED (AI Systems in Medicine), Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.
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  Data: <searchLink fieldCode="JN" term="%22101147518%22">Biomedical engineering online</searchLink> [Biomed Eng Online] 2023 Mar 22; Vol. 22 (1), pp. 28. <i>Date of Electronic Publication: </i>2023 Mar 22.
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        Value: 10.1186/s12938-023-01092-0
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              Text: 2023 Mar 22
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