Hepatic artery segmentation with 3D convolutional neural networks

Le résumé fourni par la source

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.

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