Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: In our previous study, materials consisted in abdominal CT hepatic lesions of only three patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. In this paper, thanks to a more extensively phase of data collection, available materials impressively grew to 18 patients belonging to 2-balanced classes. Several approaches were implemented to segment the region of liver and, then, to detect the ROI of the lesions. At the end of these preprocessing phases, we extracted the same morphological features of the previous work and designed an evolutionary algorithm to optimize neural network classifiers based on different subsets of features. Results and conclusion: Tests conducted on the new ANN topologies showed a higher generalization of the average performance indices regardless of the applied training, validation and test sets, confirming both the validity and the robustness of the approach of previous study even though the limited number of patients.

A Novel Approach for Hepatocellular Carcinoma Detection and Classification Based on Triphasic CT Protocol

Dimauro, Giovanni;Scardapane, Arnaldo
2017

Abstract

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: In our previous study, materials consisted in abdominal CT hepatic lesions of only three patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. In this paper, thanks to a more extensively phase of data collection, available materials impressively grew to 18 patients belonging to 2-balanced classes. Several approaches were implemented to segment the region of liver and, then, to detect the ROI of the lesions. At the end of these preprocessing phases, we extracted the same morphological features of the previous work and designed an evolutionary algorithm to optimize neural network classifiers based on different subsets of features. Results and conclusion: Tests conducted on the new ANN topologies showed a higher generalization of the average performance indices regardless of the applied training, validation and test sets, confirming both the validity and the robustness of the approach of previous study even though the limited number of patients.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/183957
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