Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography.
OBJECTIVE
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.
Shimizu
Akinobu
A
Tokyo University of Agriculture and Technology, Naka-cho 2-24-16, Koganei, Tokyo 184-8588, Japan. simiz@cc.tuat.ac.jp
Kimoto
Tatsuya
T
Kobatake
Hidefumi
H
Nawano
Shigeru
S
Shinozaki
Kenji
K
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D021621:Imaging, Three-Dimensional; D010179:Pancreas / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement; D014057:Tomography, X-Ray Computed