Liver Tumor Segmentation using Deep Learning Techniques (International Journal of Food and Nutritional Sciences, 2022)
Liver tumor segmentation plays a critical role in medical imaging analysis, especially incancer diagnosis and treatment planning. Accurate segmentation of liver tumors isessential. There has been growing interest in developing automated methods for liver tumorsegmentation, practically using deep learning algorithms. In this paper, we use theconvolutional neural networks (CNNs), on large datasets of liver images, labelled with thetumor regions. A U-net architecture is builtwith cross connections. The encoder anddecoder portion of the Unet is built using a 34 layerResNet. The model was trained on LiverTumor Segmentation challenge (LITS) dataset of liver CT scans and evaluated on a separatetest dataset. The results demonstrated that the proposed method produced greatsegmentation accuracy and an average dice score of 0.95