  Automatic organ segmentation is an important prerequisite for many
computer-aided diagnosis systems. The high anatomical variability of organs in
the abdomen, such as the pancreas, prevents many segmentation methods from
achieving high accuracies when compared to other segmentation of organs like
the liver, heart or kidneys. Recently, the availability of large annotated
training sets and the accessibility of affordable parallel computing resources
via GPUs have made it feasible for "deep learning" methods such as
convolutional networks (ConvNets) to succeed in image classification tasks.
These methods have the advantage that used classification features are trained
directly from the imaging data. We present a fully-automated bottom-up method
for pancreas segmentation in computed tomography (CT) images of the abdomen.
The method is based on hierarchical coarse-to-fine classification of local
image regions (superpixels). Superpixels are extracted from the abdominal
region using Simple Linear Iterative Clustering (SLIC). An initial probability
response map is generated, using patch-level confidences and a two-level
cascade of random forest classifiers, from which superpixel regions with
probabilities larger 0.5 are retained. These retained superpixels serve as a
highly sensitive initial input of the pancreas and its surroundings to a
ConvNet that samples a bounding box around each superpixel at different scales
(and random non-rigid deformations at training time) in order to assign a more
distinct probability of each superpixel region being pancreas or not. We
evaluate our method on CT images of 82 patients (60 for training, 2 for
validation, and 20 for testing). Using ConvNets we achieve average Dice scores
of 68%+-10% (range, 43-80%) in testing. This shows promise for accurate
pancreas segmentation, using a deep learning approach and compares favorably to
state-of-the-art methods.
