A method for mass candidate detection and an application to liver lesion detection

Maria Jimena Costa; Alexey Tsymbal; William Nguatem; Michael Suehling; S. Kevin Zhou; Dorin Comaniciu

doi:10.1117/12.877353

4 March 2011 A method for mass candidate detection and an application to liver lesion detection

Maria Jimena Costa, Alexey Tsymbal, William Nguatem, Michael Suehling, S. Kevin Zhou, Dorin Comaniciu

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79630R (2011) https://doi.org/10.1117/12.877353
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Detection and segmentation of abnormal masses within organs in Computed Tomography (CT) images of patients is of practical importance in computer-aided diagnosis (CAD), treatment planning, and analysis of normal as well as pathological regions. For intervention planning e.g. in radiotherapy the detection of abnormal masses is essential for patient diagnosis, personalized treatment choice and follow-up. The unpredictable nature of disease often makes the detection of the presence, appearance, shape, size and number of abnormal masses a challenging task, which is particularly tedious when performed by hand. Moreover, in cases in which the imaging protocol specifies the administration of a contrast agent, the contrast agent phases at which the patient images are acquired have a dramatic influence on the shape and appearance of the diseased masses. In this paper we propose a method to automatically detect candidate lesions (CLs) in 3D CTs of liver lesions. We introduce a novel multilevel candidate generation method that proves clearly advantageous in a comparative study with a state of the art approach. A learning-based selection module and a candidate fusion module are then introduced to reduce both redundancy and the false positive rate. The proposed workflow is applied to the detection of both hyperdense and hypodense hepatic lesions in all contrast agent phases, with resulting sensitivities of 89.7% and 92% and positive predictive values of 82.6% and 87.6% respectively.

Citation Download Citation

Maria Jimena Costa, Alexey Tsymbal, William Nguatem, Michael Suehling, S. Kevin Zhou, and Dorin Comaniciu "A method for mass candidate detection and an application to liver lesion detection", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630R (4 March 2011); https://doi.org/10.1117/12.877353

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