A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
This work addresses the problem of accurate pancreas segmentation for medical imaging and computer-aided diagnosis, but it is incremental as it reports only slight improvements over existing methods.
The paper tackles pancreas segmentation in abdominal CT scans, a challenging task due to high anatomical variability, and presents a bottom-up method using hierarchical patch classification and random forests, achieving 68.8% Dice coefficient and 57.2% Jaccard Index, which are comparable to or slightly better than state-of-the-art methods.
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.