Abstract
This paper describes an ensemble segmentation trained by the AdaBoost algorithm, which finds a sequence of weak hypotheses, each of which is appropriate for the distribution on training example, and combines the weak hypotheses by a weighted majority vote. In our study, a weak hypothesis corresponds to a weak segmentation process. This paper shows a procedure for generating an ensemble segmentation algorithm using AdaBoost, and applies it to a liver lesion extraction problem from a contrast enhanced abdominal CT volume. A leave-one-patient-out validation test using 16 CT volumes demonstrated the effectiveness of the generated ensemble segmentation algorithm. In addition, we evaluated the performance by applying the algorithm to unknown test data provided by the �3D Liver Tumor Segmentation Challenge 2008�.
Keywords
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Reviews
Xiang Deng
Friday 25 July 2008
This paper presents an automatic live tumor segmentation technique.
In this method, tumors are segmented using an AdaBoosting based algorithm. Morphological operation is followed to refine the segmentation.
Detailed comments:
1) Many empirically determined parameters are used to compute the 54 features in the proposed tumor segmentation algorithm.
This could make this technique sensitive to size and characteristics of training data. Could the authors briefly address this point?
2) While the focus of this contest is accuracy of tumor segmentation techniques, it would be helpful if the authors could provide some information regarding the false positives in the results, because it is an important indicator of potential of clinical application.
3) What's the difference between the segmentation algorithms for small and large lesions?
How do the authors determine small and large lesions?