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. |
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| 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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| ISSN: | 1475-925X |
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| DOI: | 10.1186/s12938-023-01092-0 |
