End-to-End Fully Automated Lung Cancer Volume Estimation System

Abstract

The volume of the tumor plays a very crucial role in deciding the stage of lung cancer which in turn helps in deciding the best treatment and its schedule. Currently used computer-based volume estimation techniques are semi-automatic with limited accuracy. For any automatic lung cancer segmentation system, lung CT scans of hundreds of patients are required along with their corresponding annotated segmentation masks. It is difficult to get accurately annotated data as cancer segmentation of CT scans done by the radiologists, is a time-consuming manual process. Also, it is subjective and prone to intra and inter-observer variability