Deep Learning-Based Liver Vessel Segmentation
Abstract: Liver vessel segmentation in computed tomography represents a highly challenging task due to the imbalanced distribution within the liver parenchyma, the small and branched vessels with decreased image contrast to surrounding tissue and in general, due to the scarcity of highresolution and -contrast images, which hampers the efficient training of deep learning-based approaches. This study applies two state-of-the-art networks, the fully convolutional nnUnet and the transformer-based VT-Unet to three publicly available datasets, 3DIRCADb, one task of the Medical Segmentation Decathlon (MSD) and the more recent LiVS dataset. The nnUnet achieved Dice scores of 0.761, 0.714, and 0.696 on the 3DIRCADb, LiVS, and MSD datasets, respectively. In contrast, the experiments with the VT-UNet resulted in Dice scores of 0.795, 0.713, and 0.610. These findings indicates good accordance of the performance of the nnUnet and the transformer-based VT-Unet, with differences regarding individual datasets. Both network variants show competitive performances regarding the current state-of-the-art, yet the need for large-scale and high-quality datasets becomes evident to further enhance the accuracy of liver vessel segmentation.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Deep Learning-Based Liver Vessel Segmentation ; volume:10 ; number:1 ; year:2024 ; pages:29-32 ; extent:4
Current directions in biomedical engineering ; 10, Heft 1 (2024), 29-32 (gesamt 4)
- Creator
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Hille, Georg
Jahangir, Tameem
Hürtgen, Janine
Kreher, Rober
Saalfeld, Sylvia
- DOI
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10.1515/cdbme-2024-0108
- URN
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urn:nbn:de:101:1-2409161535264.040282074105
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:34 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hille, Georg
- Jahangir, Tameem
- Hürtgen, Janine
- Kreher, Rober
- Saalfeld, Sylvia
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