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

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Abdominal Imaging. Computational and Clinical Applications (ABD-MICCAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8676))


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.

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Correspondence to Amal Farag .

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Farag, A., Lu, L., Turkbey, E., Liu, J., Summers, R.M. (2014). A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans. In: Yoshida, H., Näppi, J., Saini, S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science(), vol 8676. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13691-2

  • Online ISBN: 978-3-319-13692-9

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