A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans

TL;DR

This paper introduces a fully-automated bottom-up method for pancreas segmentation in abdominal CT scans, utilizing hierarchical patch classification and superpixel labeling to improve accuracy in a highly variable organ.

Contribution

A novel hierarchical two-tiered approach using patch classification and superpixel labeling with random forests for improved pancreas segmentation.

Findings

Achieved 68.8% Dice coefficient, comparable to state-of-the-art.

Used hierarchical patch and superpixel classification for better accuracy.

Validated on 80 patient CT volumes with six-fold cross-validation.

Abstract

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.