Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

作者：Shimizu Akinobu^*; Kimoto Tatsuya; Kobatake Hidefumi; Nawano Shigeru; Shinozaki Kenji

来源：International Journal of Computer Assisted Radiology and Surgery, 2010, 5(1): 85-98.

DOI：10.1007/s11548-009-0384-0

• Pancreas segmentation
• Spatial standardization
• Patient-specific probabilistic atlas
• Statistical model
• Classifier ensemble
• 3D-CT volume

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.

• 出版日期2010-1