Hepatocellular Carcinoma Segmentation within Ultrasound Images using Convolutional Neural Networks

Abstract

Hepatocellular carcinoma (HCC) is the most common malignant liver tumour. As establishing a disease is difficult even for trained medical personnel, the need for automated tools to assist in diagnosis has increased. Currently, the golden standard for HCC diagnosis is the needle biopsy, this being an invasive, dangerous method. We develop computerized, non-invasive techniques, based on ultrasound images, in order to automatically detect the HCC tumour. In this paper, we exploit the power of deep learning for the purpose of segmenting HCC within ultrasound images. Multiple deep learning methods, based on Convolutional Neural Networks (CNN), were compared for this purpose. As this segmentation problem is a difficult one, due to the complexity of the ultrasound images, but also to the high class imbalance and to the low number of available training data, a study concerning the effect of multiple loss functions used in the training phase was performed, as well.