Abstract
In this paper, we present a fully automated system that detects and segments potential liver cancer tumors from a thin slice CT data. The system is targeted toward a tumor whose volume is larger than $1 cm^3$, and is efficient as the average computation time per volume in our experiment is roughly 3.5 minutes. The system first reduces the volume size by 4x4x4 to reduce the computation and memory requirements. It then detects candidate locations as local minima of the intensity fields after a variant of textit{elastic quadratic} smoothing. It then provides a rough segmentation at each candidate by fitting a plane at sampled points near the periphery of the concave region in the intensity profiles. The rough segmentation is used to estimate the range of intensity values in the tumor, which is used to obtain a more accurate segmentation by a method originally developed for pulmonary nodules. The result of the second segmentation is interpolated at the resolution of the original data. The development of the system is a part of the 2008 3D Segmentation in the Clinic: A Grand Challenge competition. Four CT volumes containing 10 tumors were used for the development of the algorithm. Additional six CT volumes containing 10 tumors were used to test the segmentation performance.
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Reviews
Xiang Deng
Friday 25 July 2008
The authors present an automatic live tumor segmentation technique.
In this technique, candidate tumor sites are first identified and segmented by intensity thresholding and region growing. The segmented regions are then filtered using a set of hueristic criteria. The remaining segmentations are refined using competition-diffusion algorithm. Finally, segmented tumors with size below and above certain thresholds are eliminated.
Detailed comments:
1) The proposed tumor segmentation algorithm uses a lot of heuristic methods, as well as empirically determined parameters.
As a result, the algorithm could be very sensitive to the large variation of clinical liver tumor images, in terms of size, shape and contrast.
2) The number of false positive in the segmentation results would make clinical application of this technique difficult.
3) The edge of tumors may not be well-defined in the original CT images. The down-sampling used in the pre-processing stage will further blur the boundary, which could cause over-segmentation in intensity thresholding based segmentation methods.