Welcome to the InfoSci Platform
A Deep Learning Approach for Hepatocellular Carcinoma Grading

A Deep Learning Approach for Hepatocellular Carcinoma Grading

Vitoantonio Bevilacqua, Antonio Brunetti, Gianpaolo Francesco Trotta, Leonarda Carnimeo, Francescomaria Marino, Vito Alberotanza, Arnaldo Scardapane
Copyright: © 2020 |Pages: 19
ISBN13: 9781799804147|ISBN10: 1799804143|EISBN13: 9781799804154
DOI: 10.4018/978-1-7998-0414-7.ch021
Cite Chapter Cite Chapter

MLA

Bevilacqua, Vitoantonio, et al. "A Deep Learning Approach for Hepatocellular Carcinoma Grading." Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 353-371. https://doi.org/10.4018/978-1-7998-0414-7.ch021

APA

Bevilacqua, V., Brunetti, A., Trotta, G. F., Carnimeo, L., Marino, F., Alberotanza, V., & Scardapane, A. (2020). A Deep Learning Approach for Hepatocellular Carcinoma Grading. In I. Management Association (Ed.), Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 353-371). IGI Global. https://doi.org/10.4018/978-1-7998-0414-7.ch021

Chicago

Bevilacqua, Vitoantonio, et al. "A Deep Learning Approach for Hepatocellular Carcinoma Grading." In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 353-371. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0414-7.ch021

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.