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|Title:||Computer-aided focal liver lesion detection|
|Keywords:||3D focal liver lesion detection|
|Source:||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. https://doi.org/10.1007/s11548-013-0832-8|
|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|
|Appears in Collections:||Staff Publications|
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