讲座题目：Hierarchical Segmentation of Multimodality MRI Human Brain Tumors - Towards a Comprehensive Characterization of Tumors via Structural and Diffusion Tensor Magnetic Resonance Imaging

主讲人: 蔡宏民博士

时   间: 2008.5.21, 16:00-17:00pm 

地   点: F203, 四维创新实验室

摘   要: The talk aims at creating a multi-modal profile of tissue components that will not only help in delineating tumor and edema from healthy tissue, distinguishing between enhancing and non-enhancing tumors, but also produce a probabilistic characterization of tissue around the tumor to determine abnormal regions that may have a tendency to convert to tumor in the future. The multimodality profile is generated by a combination of five structural MR images, FLAIR, T1, Gadolium enhanced T1 (GAD), DWI and B0, and two scalar maps, including Fractional Anisotropy (FA) and Apparant Diffusion Coefficient (ADC) computed from diffusion tensor images (DTI), creating a seven-dimensional intensity feature vector for each voxel. The tumor-grade- specific ground truth identified by doctors, are trained through a newly proposed hierarchical Support Vector Machine (SVM) based on spatial and texture features, the system achieves near-perfect characterization of tumor components (enhancing and non-enhancing), edema and healthy tissue with a 90 – 95 % classification rate together with almost 0 false positive rate. In addition to this hard tissue segmentation, the framework also provides probability profile for tissue, indicating unhealthy regions that may have a tendency to convert to tumorous tissue, hence providing a better characterization of the resection margin. The classifiers, trained on a dataset of 22 patients, can be applied to a new dataset with a success rate of 80% for classification, as has been tested using a leave-one-out paradigm on these 22 datasets. The multimodality processing pipeline that we have designed is general and is applicable to any study that has multi-modal data acquisition. 　

　

主讲人简介:
Dr. CAI Hongmin received his B.S and M.S degrees both from Harbin Institute of Technology, China. He obtained his PhD degree in applied mathematics from the University of Hong Kong in 2007. His research interest includes biomedical image analysis and biometric recognition.