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A Deep Learning Approach for Hepatocellular Carcinoma Grading
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Author(s): Vitoantonio Bevilacqua (Polytechnic University of Bari, Italy), Antonio Brunetti (Polytechnic University of Bari,Italy), Gianpaolo Francesco Trotta (Polytechnic University of Bari, Italy), Leonarda Carnimeo (Polytechnic University of Bari, Italy & Apulia Intelligent Systems Ltd, Italy), Francescomaria Marino (Polytechnic University of Bari, Italy), Vito Alberotanza (University of Bari, Italy)and Arnaldo Scardapane (University of Bari, Italy)
Copyright: 2020
Pages: 19
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch021
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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, thus 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: Materials consist in abdominal CT hepatic lesion from 18 patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. Several approaches are implemented to segment the region of liver and, then, detect the lesion ROI. Results: A Deep Learning approach using Convolutional Neural Network is followed for HCC grading. The obtained good results confirm the robustness of the segmentation algorithms leading to a more accurate classification.
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