Automatic infarct segmentation in rat brain MR images after stroke using an adaptive deformable model

Abstract

Experimental ischemic stroke models play an important role in realizing the mechanism of cerebral ischemia and evaluating the development of pathological extent. An efficient and reliable image segmentation tool to automatically identify the infarct region in the diffusion weighted imaging (DWI) and T2-weighted MRI (T2WI) images is critical for subsequent processing applications. This paper develops an automatic infarct segmentation algorithm in both rat brain DWI and T2WI images after stroke for further evaluation of neurological damages. The proposed framework consists of four major steps including image preprocessing, image registration, image enhancement, and infarct segmentation. To achieve complete automation, the input rat brain is first divided into two hemispheres, from which the initial infarct mask is acquired after a series of image registration, image subtraction, and image enhancement processes. Subsequently, an adaptive deformable model is exploited to perform infarct region segmentation. The proposed deformable model employs two-phase level set evolution, which is regularized by a local region integration. The integration of the difference between the local intensities and the global mean intensity is restricted in the inward and outward normal directions to minimize the influence of the intensity inhomogeneity. Moreover, the time step is dynamically modified towards annealing for performance refinement. Massive MR images were utilized to evaluate this new infarct segmentation algorithm. Adequate infarct segmentation results were obtained, which outperformed other competitive methods both qualitatively and quantitatively. Our infarct segmentation framework is of potential in providing a decent tool to facilitate preclinical stroke investigation and relevant neuroscience research using DWI and T2WI images.