Implementation of Liver Segmentation from Computed Tomography (CT) Images Using Deep Learning

Abstract

Liver segmentation from computed tomography (CT) images has grown significantly in importance in the field of medical image processing in the last few years. It is the first and most crucial step in any computerized technique for the automatic detection of liver disease, liver volume measurement, and 3D liver volume rendering. The diagnosis and treatment of liver cancer depend heavily on the segmentation of the liver from CT images to get liver volumetric data, but manual segmentation is a strenuous and time-consuming process. The procedure can be accelerated, simplified, and made less error-prone by using deep learning methods. Image segmentation based on deep learning techniques has gained widespread acceptance due to its robustness, efficiency, and it's reproducible nature. Therefore, in this paper, using UNet, MONAI (Medical Open Network for Artificial Intelligence) and PyTorch framework, a deep-learning model to segment the liver from publicly available CT scan dataset was developed. The same ideas that underlie this model for segmenting the liver will allow to create models for segmenting other organs or malignancies using CT data. The goal is to develop a liver segmentation model that can quickly and accurately extract the liver from any given CT image with an accuracy that is on par of manual segmentation performed by a skilled radiologist.