Single click volumetric segmentation of abdominal organs in Computed Tomography images

ABSTRACT ♠ Current segmentation techniques require user intervention to fine-tune thresholds and parameters, plot initial contours, refine seed placement, and engage in other optimization strategies. This can cause difficulties for physicians trying to use segmentation tools as they may not have the time or resources to overcome steep learning curves. In order to segment volumetric regions from sequential slices of computed tomography (CT) images with minimal user intervention, we propose an algorithm based on volumetric seeded region growing that employs an adaptive and prioritized expansion. This algorithm requires a user only to identify a voxel in an organ to perform volumetric segmentation. This approach overcomes the need to manually select threshold values for specific organs by analyzing the histogram of voxel similarity to automatically determine a stopping criterion. The homogeneity criterion used for region growth in this approach is calculated from volumetric texture descriptors derived from co-occurrence matrices which consider voxel-pairs in a 3-dimensional neighborhood of a given voxel. Preliminary segmentation results of the kidneys, spleen, and liver were obtained on 3D data extracted from 700 sequential CT images from various studies collected by Northwestern Memorial Hospital. We believe this approach to be a viable segmentation technique that requires significantly less user intervention when compared to other techniques by necessitating only one user intervention, namely the selection of a single seed point

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