Meeting Banner
Abstract #1746

A New Adaptive Markov Random Field Model in a Coupled Level Set Framework for Bladder Wall Segmentation in MR Images

Hao Han1, Lihong Li2, Chaijie Duan3, Hao Zhang1, Zhengrong Liang1

1Dept. of Radiology, Stony Brook University, Stony Brook, NY, United States; 2Dept. of Engineering Science & Physics, College of Staten Island of the City University of New York, Staten Island, NY, United States; 3Dept. of Biomedical Engineering, Tsinghua University, Shenzhen, Guangdong, China

Bladder cancer is the fifth leading cause of cancer related death, especially for aged males in the United States. Early detection of bladder lesion is so crucial that advanced techniques are required to precisely and safely differentiate tumors from the normal bladder wall. In this work, we developed a novel approach to precisely segment the inner border and outer border of the bladder wall, and it was shown that our new approach outperforms the previous approach developed in our group. The bladder lesion can be visualized through a 3-D rendering model on wall thickness calculated from the segmented bladder wall.