Liver registration for the follow-up of hepatic tumors
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A cascade learning method for liver lesion detection in CT images
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Snake model-based lymphoma segmentation for sequential CT images
Computer Methods and Programs in Biomedicine
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In follow-up CT examinations of cancer patients, therapy success is evaluated by estimating the change in tumor size from diameter or volume comparison between corresponding lesions. We present an algorithm that automatizes the detection of matching lesions, given a baseline segmentation mask. It is generally applicable and does not need an organ mask or CAD findings, only a coarse registration of the datasets is required. In the first step, lesion candidates are identified in a local area based on gray value filtering and detection of circular structures using a Hough transform. On all candidate voxels, a template matching is performed minimizing normalized cross-correlation. The method was evaluated on clinical follow-up data comprising 94 lung nodules, 107 liver metastases, and 137 lymph nodes. The ratio of correctly detected lesions was 96%, 84% and 85%, respectively, at an average computation time of 0.9 s per lesion on a standard PC.