Automatic pancreas segmentation using coarse-to-fine superpixel labeling

Abstract

Accurate automatic detection and segmentation of abdominal organs from CT images is important for quantitative and qualitative organ tissue analysis, detection of pathologies, surgical assistance as well as computer-aided diagnosis (CAD). In general, the large variability of organ locations, the spatial interaction between organs that appear similar in medical scans and orientation and size variations are among the major challenges of organ segmentation. The pancreas poses these challenges in addition to its flexibility which allows for the shape of the tissue to vastly change. In this chapter, we present a fully automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method is a four-stage system based on a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels. System components consist of the following: (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. Evaluation of the approach is conducted on a database of 80 manually segmented CT volumes in sixfold cross validation. Our achieved results are comparable, or better to the stateof- the-art methods (evaluated by “leave-one-patient-out”), with a Dice coefficient of 70.7% and Jaccard Index of 57.9%. The computational efficiency of the proposed approach is drastically improved in the order of 6–8 min, compared to other methods of ≥10 hours per testing case.