Improving Atlas-Based Medical Image Segmentation with a Relaxed Object Search

摘要

Medical image segmentation using 3D probabilistic atlases has been actively pursued to avoid the time-consuming involvement of experts in manual object (organ) delineation for quantitative analysis. By mapping a new 3D image onto the reference coordinate system of the atlas, built for some organ of interest, these techniques take a binary decision based on the probability of each voxel to be part of that organ. However, image-based techniques have also been proposed to refine object delineation at the initial position given by the atlas-based segmentation. In this paper, we relax this condition for delineation refinement by moving an atlas based on the prior probability map to search for the organ around that initial position. Our method uses the multi-scale parameter search algorithm with a suitable criterion function to evaluate automatic 3D organ delineations, as obtained by the image foresting transform algorithm in an uncertainty region of the atlas. Experiments with eight organs in CT and MR images have indicated that our method can improve atlas-based segmentation with statistical significance. Moreover, the relaxed object search consistently found the organ with higher accuracy outside the position obtained by the atlas, which reinforces our claim.