A Bottom-up Approach for Pancreas Segmentation using Cascaded
Superpixels and (Deep) Image Patch Labeling
release_hx2h24o4c5c2bb7hbysm7vvqcq
by
Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald
M. Summers
2015
Abstract
Robust automated organ segmentation is a prerequisite for computer-aided
diagnosis (CAD), quantitative imaging analysis and surgical assistance. For
high-variability organs such as the pancreas, previous approaches report
undesirably low accuracies. We present a bottom-up approach for pancreas
segmentation in abdominal CT scans that is based on a hierarchy of information
propagation by classifying image patches at different resolutions; and
cascading superpixels. There are four stages: 1) decomposing CT slice images as
a set of disjoint boundary-preserving superpixels; 2) computing pancreas class
probability maps via dense patch labeling; 3) classifying superpixels by
pooling both intensity and probability features to form empirical statistics in
cascaded random forest frameworks; and 4) simple connectivity based
post-processing. The dense image patch labeling are conducted by: efficient
random forest classifier on image histogram, location and texture features; and
more expensive (but with better specificity) deep convolutional neural network
classification on larger image windows (with more spatial contexts). Evaluation
of the approach is performed on a database of 80 manually segmented CT volumes
in six-fold cross-validation (CV). Our achieved results are comparable, or
better than the state-of-the-art methods (evaluated by
"leave-one-patient-out"), with Dice 70.7% and Jaccard 57.9%. The computational
efficiency has been drastically improved in the order of 6~8 minutes, comparing
with others of ~10 hours per case. Finally, we implement a multi-atlas label
fusion (MALF) approach for pancreas segmentation using the same datasets. Under
six-fold CV, our bottom-up segmentation method significantly outperforms its
MALF counterpart: (70.7 +/- 13.0%) versus (52.5 +/- 20.8%) in Dice. Deep CNN
patch labeling confidences offer more numerical stability, reflected by smaller
standard deviations.
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