Liver Tumor Segmentation From Computed Tomography Images Through Convolutional Neural Networks

Cancer is a severe affection nowadays, leading to death in many situations. Among the liver tumors, the most often met is Hepatocellular Carcinoma (HCC), being present for 75% of the primary liver cancer patients. Despite its’ invasive character, the most reliable cancer diagnosis method is biopsy. For performing both non-invasive and accurate disease assessment, computerized techniques are required. Tumor semantic segmentation is useful in this context, achieving tumor detection, localization, and extension estimation at the same time. In our current research, we developed and comparatively assessed high performance methods for segmenting liver tumor, based on Convolutional Neural Networks (CNN), as UNet, UNet++ and DeepLabV3+, involving two CT image datasets in our experiments. The assessment was performed by considering both the polar and cartesian image representations. At the end, a maximum DICE value of 80.92%, a maximum IoU value of 69.55%, respectively a maximum accuracy value of 99.82% resulted.