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Title: Computer-aided focal liver lesion detection
Authors: Chi, Y.
Zhou, J.
Venkatesh, S.K. 
Huang, S.
Tian, Q.
Hennedige, T.
Liu, J.
Keywords: 3D focal liver lesion detection
Background subtraction
Computer-aided detection
Non-lesion modeling
Issue Date: Jul-2013
Citation: Chi, Y., Zhou, J., Venkatesh, S.K., Huang, S., Tian, Q., Hennedige, T., Liu, J. (2013-07). Computer-aided focal liver lesion detection. International Journal of Computer Assisted Radiology and Surgery 8 (4) : 511-525. ScholarBank@NUS Repository.
Abstract: Purpose: Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes. Method: A hybrid generative-discriminative framework is proposed. It first uses a generative model to describe non-lesion components and then identifies all candidate FLLs within a 3D liver volume by eliminating non-lesion components. It subsequently uses a discriminative approach to suppress false positives with the advantage of tumoroid, a novel measurement combining three shape features spherical symmetry, compactness and size. Results: This method was tested on 71 abdominal CT datasets (5,854 slices from 61 patients, with 261 FLLs covering six pathological types) and evaluated using the free-response receiver operating characteristic (FROC) curves. Overall, it achieved a true positive rate of 90 % with one false positive per liver. It degenerated gently with the decrease in lesion sizes to 30 ml. It achieved a true-positive rate of 36 % when tested on the lesions less than 4 ml. The average computing time of the lesion detection is 4 min and 28 s per CT volume on a PC with 2.67 GHz CPU and 4.0 GB RAM. Conclusions: The proposed method is comparable to the radiologists' visual investigation in terms of efficiency. The tool has great potential to reduce radiologists' burden in going through thousands of images routinely. © 2013 CARS.
Source Title: International Journal of Computer Assisted Radiology and Surgery
ISSN: 18616410
DOI: 10.1007/s11548-013-0832-8
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