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

Rickmann, Anne-Marie; Senapati, Jyotirmay; Kovalenko, Oksana; Peters, Annette; Bamberg, Fabian und Wachinger, Christian (17. September 2022): AbdomenNet: deep neural network for abdominal organ segmentation in epidemiologic imaging studies. In: BMC Medical Imaging, Bd. 22

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 imaging studies, namely the German National Cohort, UK Biobank, and Kohorte im Raum Augsburg. We chose a total of 61 MRI scans across the three studies for training an ensemble of segmentation networks, which segment eight abdominal organs. Our network presents a novel combination of octave convolutions and squeeze and excitation layers, as well as training with stochastic weight averaging. Results Our experiments demonstrate that it is beneficial to combine data from different imaging studies to train deep neural networks in contrast to training separate networks. Combining the water and opposed-phase contrasts of the Dixon sequence as input channels, yields the highest segmentation accuracy, compared to single contrast inputs. The mean Dice similarity coefficient is above 0.9 for larger organs liver, spleen, and kidneys, and 0.71 and 0.74 for gallbladder and pancreas, respectively. Conclusions Our fully automated pipeline provides high-quality segmentations of abdominal organs across population studies. In contrast, a network that is only trained on a single dataset does not generalize well to other datasets.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Klinikum der LMU München > Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1471-2342
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892