Organ segmentation of CT volumes is a basic function of computer-aided diagnosis and surgery-assistance systems.
Many of these systems implement organ segmentation methods that are limited to specific organs and
that are not robust in dealing with inter-subject differences of organ shape or position. In this paper, we propose
an automated method for multi-organ segmentation of abdominal 3D CT volumes by using a patient-specific,
weighted-probabilistic atlas for organ position. This is achieved in a two-step process. First, we prepare for
segmentation by dividing an atlas database into multiple clusters. This is done using pairs of a training image
and the corresponding manual segmentation data set. In the next step, we choose a cluster whose template
image is the most similar to the target image. We then weight all of the atlases in the selected cluster by calculating
the similarities between the atlases and the target image to dynamically generate a specific probabilistic
atlas for each target image. We use the generated probabilistic atlas in MAP estimation to obtain a rough
segmentation result and then refine it by using a graph-cut method. Our method can simultaneously segment
four organs: the liver, spleen, pancreas and kidneys. Our weighting scheme greatly reduces segmentation error
due to inter-subject differences. We applied our method to 100 cases of CT volumes and thus showed that it
could segment the liver, spleen, pancreas and kidneys with Dice similarity coefficients of 95.2%, 89.7%, 69.6%,
and 89.4%, respectively.