Abstract #300054

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JSM 2003 Abstract #300054
Activity Number: 251
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300054
Title: Validation of Automated Probabilistic Image Segmentation of Brain Tumors
Author(s): Kelly Zou*+ and Simon Warfield and William Wells and Ron Kikinis
Companies: Harvard University Medical School and Harvard University and Harvard University Medical School and Brigham and Women's Hospital
Address: Dept. Health Care Policy, Boston, MA, 02115,
Keywords: ROC ; Mutual Information ; Similarity Coefficient ; EM Algorithm ; Mixture Modeling ; Pixel Data
Abstract:

The validity of brain tumor segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts' manual segmentations by an EM-algorithm. The distribution functions of the tumor and control pixel data parametrically were assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumor cases of three different tumor types, each consisting of large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation.

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