Multiple sclerosis lesion detection with local multimodal Markovian analysis and cellular automata ‘GrowCut’

Multiple sclerosis affect over 2.5 million people world‐wide. This autoimmune disease of the central nervous system causes damage to the insulating myelin sheaths around the axons in the brain. The disease progresses at different rates in different people and can have periods of remission and relapse. A fast and accurate method for evaluating the number and size of MS lesions in the brain is a key component in evaluating the progress of the disease and the efficacy of treatments. Manual segmentation is slow and difficult and the results can be somewhat subjective. It requires a physician to consider several MRI slices across multiple modalities. The power and speed of computer systems provide an obvious avenue to help. While many automated methods exist, they have not reached human‐level accuracy of the segmentation results. There exists a need for a robust, fast and accurate method to improve the results of automatic MS lesion segmentation methods. We propose a post‐processing stage to improve the segmentation results of an existing system. It uses two different strategies to improve the segmentation results of an automated system based on whole‐brain tissue classification and lesion detection. The first strategy leverages the current processing system at a granularity finer than the whole brain to detect lesions at a local level. This reflects the way that a physician considers only a part of the brain at a time. It then combines the series of local results to produce a whole‐brain segmentation. This approach better captures the local lesion properties and produces encouraging results, with a general improvement in the detection rate of lesions. The second method dives deeper and looks at the individual voxel level. Just as a physician might look more closely at a lesion, it considers the local neighborhood around a lesion detection. The method selects seed points from the existing results and uses a region growing method based on cellular automata. It grows the lesion areas based on a local neighborhood similarity in intensity. Over the eleven patients examined, some results improved over the base case and show the efficiency of the proposed approach.