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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Amal Farag , Le Lu , Evrim Turkbey , Jiamin Liu , Ronald M. Summers

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. More... »

PAGES

103-113

Book

TITLE

Abdominal Imaging. Computational and Clinical Applications

ISBN

978-3-319-13691-2
978-3-319-13692-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-13692-9_10

DOI

http://dx.doi.org/10.1007/978-3-319-13692-9_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009316051


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