3D Segmentation for Multi-Organs in CT Images - Bajger, Mariusz; Lee, Gobert; Caon, Martin (ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2013)
The study addresses the challenging problem of automatic segmentation of the human anatomy needed for radiation dose calculations. Three-dimensional extensions of two well-known state-of-the art segmentation techniques are proposed and tested for usefulness on a set of clinical CT images. The new techniques are 3D Statistical Region Merging (3D-SRM) and 3D Efficient Graph-based Segmentation (3D-EGS). Segmentations of eight representative tissues (lungs, stomach, liver, heart, kidneys, spleen, bones and the spinal cord) were tested for accuracy using the Dice index, the Hausdorff distance and the $H_t$ index. The 3D-SRM outperformed 3D-EGS producing the average (across the 8 tissues) Dice index, the Hausdorff distance, and the $H_2$ of $0.89$, $12.5$~mm and $0.93$, respectively.