Iterative 3D CNN Based Segmentation of Vascular Trees in Liver CT

The segmentation of vascular systems is a challenging task since their sizes and structures vary greatly so that the spatial context becomes highly important. For further clinical analysis of the vascular system it is important to create a connected vascular tree starting from the main trunk, following the tree structure up to small branches. To address these issues, we propose a new iterative segmentation model that recursively evolves a segmentation of a vasculature by following its tree structure. Our iterative CNN alternates between three steps: First, a 3D segmentation of a sub-region is performed. Second, branches that are not part of the currently analyzed branch are removed and third, subsequent sub-regions are detected. These steps are repeated until the entire vascular system is segmented. We trained, validated and tested our model on 82 CT images. We showed that, in comparison to state of the art methods, our new model generates a more accurate segmentation, resulting in an improvement of the Dice score of 7 % and a reduction of the Hausdorff distance of approximately 20 %.