Ischemic Stroke Lesion Segmentation by Analyzing MRI Images Using Deep Convolutional Neural Networks

Deep Learning proves to be the best way towards image classification tasks replacing ensembles with handcrafted features. It also has applications in medical imaging such as classification of tumorous and non-tumorous part from rest of the MRI image. Lesions is one such disease occurring in brain due to lack of oxygen during stroke. Automatic segmentation of ischemic stroke lesions is the perfect problem solved by using deep learning methods especially Convolution Neural Networks (CNNs). We present a patch based approach using multi-scale convolution layers with three pathways to segment lesions using 6 modalities of MRI images. The accuracy achieved using this method is comparable to other methods.