  Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD)
system to detect pathologies and perform quantitative analysis. For
anatomically high-variability abdominal organs such as the pancreas, previous
segmentation works report low accuracies when comparing to organs like the
heart or liver. In this paper, a fully-automated bottom-up method is presented
for pancreas segmentation, using abdominal computed tomography (CT) scans. The
method is based on a hierarchical two-tiered information propagation by
classifying image patches. It labels superpixels as pancreas or not via pooling
patch-level confidences on 2D CT slices over-segmented by the Simple Linear
Iterative Clustering approach. A supervised random forest (RF) classifier is
trained on the patch level and a two-level cascade of RFs is applied at the
superpixel level, coupled with multi-channel feature extraction, respectively.
On six-fold cross-validation using 80 patient CT volumes, we achieved 68.8%
Dice coefficient and 57.2% Jaccard Index, comparable to or slightly better than
published state-of-the-art methods.
