A Deep Learning Approach for Hepatocellular Carcinoma Grading

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.