Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching

Abstract

A fully automatic system for segmentation of the liver from CT scans is presented. The core of the method consists of a voxel labeling procedure where the probability that each voxel is part of the liver is estimated using a statistical classifier (k-nearest-neighbor) and a set of features. Several features encode positional information, obtained from a multi-atlas registration procedure. In addition, pre-processing steps are carried out to determine the vertical scan range of the liver and to rotate the scan so that the subject is in supine position, and post-processing is applied to the voxel classification result to smooth and improve the final segmentation. The method is evaluated on 10 test scans and performs robustly, as the volumetric overlap error is 12.5% on average and 15.3% for the worst case. A careful inspection of the results reveals, however, that locally many errors are made and the localization of the border is often not precise. The causes and possible solutions for these failures are briefly discussed.

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