An Optimization-based Technique Applied by Mask RCNNfor Liver Tumor Detection and Segmentation on CTScan Images

• Main Authors: Chun-Tai Lin, 林均泰
• Other Authors: Yeon-sung Lin
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/fj4x7k

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Liver cancer has become one of the leading cause of death worldwide. While focusing on Taiwan, liver cancer is the leading cause of death from cancer in males and the second leading cause in females. In order to determine the treatment options earlier, the accuracy and efficiency play important roles in liver cancer diagnosing. The traditional way to diagnose liver cancer is to identify whether or not the CT slices contain the tumor manually, which will reduce the working efficiency for doctor and affect the healthcare quality. As a result, this study proposes a framework to automatically segment liver and its lesion in CT volumes for assisting doctors to efficiently diagnose liver cancer. We train and cascade two Mask R-CNN models which are used to segment liver and lesions separately. The first model of Mask R-CNN will segment liver from the CT slices as RoI input for the second model. The second Mask R-CNN will segment lesions with the predicted liver RoIs from previous model. While training the Mask R-CNN model, we enhance the weight of segmentation loss function and apply penalty metrics to add on weights on the pixels classified incorrectly. We train on a public dataset which contains 130 abdominal CT volumes of patients offered by MICCAI 2017 LiTS. The framework proposed by the paper achieves Dice score 94.1% and 75.2% respectively for liver and liver tumor segmentation.