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. Further, owing to the irregular shape of the cancerous tumor, accurate volume estimation becomes a challenge with regular convolution models. This paper proposes an end-to-end automatic tumor volume estimation model that estimates volume using the GPR (Gaussian Process Regression) interpolation method. The proposed modified cancer segmentation model uses deformable convolutions. This modification offers a higher segmentation accuracy in terms of IoU (Intersection over Union) and clearly defined nodule boundaries with correct retention of the nodule shape. The research was undertaken in collaboration with Nanavati Hospital, Mumbai, and all the models were validated on a real dataset obtained from the hospital. The proposed model gives a mean segmentation IoU (Intersection over Union) of 0.9035 and a volume estimation accuracy of 93.13% which are almost 5% and 3% higher than 0.8548 and 90.51% which are the corresponding results obtained using a standard U-net++ algorithm.