3D Segmentation for Multi-Organs in CT Images

Mariusz Bajger, Gobert Lee, Martin Caon


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.


Voxel model, image segmentation, statistical region merging, efficient graph-based segmentation, full-body CT

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