"Lung vessel segmentation in CT images using graph cuts"
Zhiwei Zhai, Marius Staring and Berend C. Stoel
Accurate lung vessel segmentation is an important operation for lung CT analysis. Hessian-based filters are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is inaccurate. Some literature turns to graph cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. In order to make the graph representation computationally tractable, voxels that are considered clearly background are removed using a low threshold on the vesselness map. The graph structure is then established based on the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph cuts optimization framework. We optimized the parameters and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used the 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volumes dataset show that the proposed method produced a more accurate vessels segmentation. For the VESSEL12 dataset, our method obtained a competitive performance with an area under the ROC of 0.975, especially among the binary submissions.