Hepatic artery segmentation with 3D convolutional neural networks

Farina Kock; Grzegorz Chlebus; Felix Thielke; Andrea Schenk; Hans Meine

doi:10.1117/12.2607253

4 April 2022 Hepatic artery segmentation with 3D convolutional neural networks

Farina Kock, Grzegorz Chlebus, Felix Thielke, Andrea Schenk, Hans Meine

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331O (2022) https://doi.org/10.1117/12.2607253
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

The segmentation of liver vessels is a crucial task for liver surgical planning. In selective internal radiation therapy, a catheter has to be placed into the hepatic artery, injecting radioactive beads to internally destroy tumor tissue. Based on a set of 146 abdominal CT datasets with expert segmentations, we trained three-level 3D U-Nets with loss-sensitive re-weighting. They are evaluated with respect to different measures including the Dice coefficient and the mutual skeleton coverage. The best model incorporates a masked loss for the liver area, which achieves a mean Dice coefficient of 0.56, a sensitivity of 0.69 and a precision of 0.66.

Conference Presentation

Citation Download Citation

Farina Kock, Grzegorz Chlebus, Felix Thielke, Andrea Schenk, and Hans Meine "Hepatic artery segmentation with 3D convolutional neural networks", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331O (4 April 2022); https://doi.org/10.1117/12.2607253

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