Brain Lesion Segmentation through Physical Model Estimation

Abstract

Segmentations of brain lesions from Magnetic Resonance (MR) images is crucial for quantitative analysis of lesion populations in neuroimaging of neurological disorders. We propose a new method for segmenting lesions in brain MRI by inferring the underlying physical models for pathology. We use the reaction-diffusion model as our physical model, where the diffusion process is guided by real diffusion tensor fields that are obtained from Diffusion Tensor Imaging (DTI). The method performs segmentation by solving the inverse problem, where it determines the optimal parameters for the physical model that generates the observed image. We show that the proposed method can infer reasonable models for multiple sclerosis (MS) lesions and healthy MRI data. The method has potential for further extensions with different physical models or even non-physical models based on existing segmentation schemes.

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://www.nihroadmap.nih.gov /bioinformatics