Please use this identifier to cite or link to this item:
|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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 7, 2018
WEB OF SCIENCETM
checked on Jan 29, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.