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Titel:

AbdomenNet: deep neural network for abdominal organ segmentation in epidemiologic imaging studies.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Rickmann, Anne-Marie; Senapati, Jyotirmay; Kovalenko, Oksana; Peters, Annette; Bamberg, Fabian; Wachinger, Christian
Abstract:
BACKGROUND: Whole-body imaging has recently been added to large-scale epidemiological studies providing novel opportunities for investigating abdominal organs. However, the segmentation of these organs is required beforehand, which is time consuming, particularly on such a large scale. METHODS: We introduce AbdomentNet, a deep neural network for the automated segmentation of abdominal organs on two-point Dixon MRI scans. A pre-processing pipeline enables to process MRI scans from different imagi...     »
Zeitschriftentitel:
BMC Med Imaging
Jahr:
2022
Band / Volume:
22
Heft / Issue:
1
Volltext / DOI:
doi:10.1186/s12880-022-00893-4
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/36115938
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie
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