Automatic Detection of Ischemic Stroke Lesion from Multimodal MR Image

During ischemic stroke, the most common kind of blocking in an artery, the brain cells start to die due to lack of oxygen and nutrients. Such strokes need immediate diagnosis and treatment to minimize the brain damage. Recent studies have shown the potential of using magnetic resonance imaging (MRI) in diagnosing ischemic stroke. Reviewing hundreds of slices produced by MRI, however, takes a lot of time and can lead to numerous human errors. It is widely accepted by the medical practitioners that automated segmentation methods for ischemic stroke lesions could significantly speed up the beginning of a patient's treatment. The automated segmentation can locate the tissue with lesions and give an estimate of its volume, which helps in the clinical practice by providing a better assessement and evaluation of the risks of each treatment. These reasons highlight the need for a fully automatic ischemic stroke lesion segmentation approach using a flexible, fast and effective deep neural network. We have also investigated the use of zero-cantering and normalization of intensity for smooth variation of the intensity over the tissues as a pre-processing step. A relative study has been done to prove the efficacy of the proposed CNN model on Ischemic Stroke Lesion Segmentation Challenge 2015 database (ISLES2015).