User: Guest  Login
More Searchfields
Simple search
Title:

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

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
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...     »
Journal title abbreviation:
BMC Med Imaging
Year:
2022
Journal volume:
22
Journal issue:
1
Fulltext / DOI:
doi:10.1186/s12880-022-00893-4
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36115938
TUM Institution:
Institut für Diagnostische und Interventionelle Radiologie
 BibTeX