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Proceedings Paper

Pancreas and cyst segmentation
Author(s): Konstantin Dmitriev; Ievgeniia Gutenko; Saad Nadeem; Arie Kaufman
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Paper Abstract

Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842C (21 March 2016); doi: 10.1117/12.2216537
Show Author Affiliations
Konstantin Dmitriev, Stony Brook Univ. (United States)
Ievgeniia Gutenko, Stony Brook Univ. (United States)
Saad Nadeem, Stony Brook Univ. (United States)
Arie Kaufman, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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