PURPOSE: We propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation. METHODS: The algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results. RESULTS: The effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%. CONCLUSIONS: This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.
PURPOSE: We propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation. METHODS: The algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results. RESULTS: The effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%. CONCLUSIONS: This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.
Authors: Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato Journal: Med Image Anal Date: 2015-07-04 Impact factor: 8.545
Authors: Zhoubing Xu; Ryan P Burke; Christopher P Lee; Rebeccah B Baucom; Benjamin K Poulose; Richard G Abramson; Bennett A Landman Journal: Med Image Anal Date: 2015-05-21 Impact factor: 8.545
Authors: Matthias Hammon; Alexander Cavallaro; Marius Erdt; Peter Dankerl; Matthias Kirschner; Klaus Drechsler; Stefan Wesarg; Michael Uder; Rolf Janka Journal: J Digit Imaging Date: 2013-12 Impact factor: 4.056
Authors: Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Yuki Suzuki; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2012