3D segmentation for multi-organs in CT images

Abstract

The study addresses the challenging problemof automatic segmentation of the human anatomy needed for radiation dose calculations. Three-dimensional extensions of two well-known stateof-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 Ht index. The 3D-SRM outperformed 3D-EGS producing the average (across the 8 tissues) Dice index, the Hausdorff distance, and the H2 of 0.89, 12.5 mm and 0.93, respectively.