Abstract
In this paper a specific method is presented to facilitate the semi-automatic segmentation of liver metastases in CT images. Accurate and reliable segmentation of tumors is e.g. essential for the follow-up of cancer treatment. The core of the algorithm is a level set function. The initialization is provided by a spiral-scanning technique based on dynamic programming. The level set evolves according to a speed image that is the result of a statistical pixel classification algorithm with supervised learning. This method is tested on CT images of the abdomen and compared with manual delineations of liver tumors.
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Source Code and Data
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Reviews
Xiang Deng
Friday 25 July 2008
This paper presents a level-set based semi-automatic live tumor segmentation technique.
The segmentation is initialized using a user-defined ROI. The level set is evolved according to a speed function computed using fuzzy pixel classification technique.
Detailed comments:
1) What makes liver tumor segmentation a challenging task is the large variation of lesion's shape, size and contrast in CT images. A tumor segmentation technique with potential of clinical application should be able to handle both homogeneous and round shaped lesions, as well as not well-defined ones.
2) In the last paragraph of "Discussion" section, the authors said that "user intelligence was reduced when placing the two seeds". From my point of view, it would be helpful to test the reproducibility of the proposed technique on the training and testing data, before making such a conclusion.
3) What is the stop criterion of the level-set evolution?