Segmentation of Lung Tumors from Chest CT Using U-Net

Abstract

　　According to the report about cause of death in 2018 in Taiwan the morbidity and mortality rate of lung cancer are constantly increasing in recent years A research result in America showed that diagnosis with CT in early stage relatively reduces the rate of death from lung cancer for high risk populations 　　In this Thesis a segmentation method of lung tumors from chest CT based on U-net is proposed The goal is to automatically segment the lung tumor region on 3D volume then produce a mask for each slice of CT scans The system combines the U-Net based fully convolutional network with an additional classifier block in order to eliminate the non-tumor slices as much as possible Besides since data imbalance is a common issue in medical image segmentation Tversky Loss is used to optimize our model to get better performance in segmentation

Date of Award	2020
Original language	English
Supervisor	Shen-Chuan Tai (Supervisor)