Brain Ischemic Stroke Segmentation Using Ensemble Deep Learning

Strokes are a leading cause of premature mortality in wealthy nations, and early treatment assistance can significantly prolong a patient’s life. The primary rehabilitative step in the therapy of stroke is determined by how quickly the lesion is identified from MRI images. This will be an essential tool for determining the extent of brain cell damage. However, manual lesion identification takes time and is susceptible to both intra- and inter-observer inconsistencies. In light of this, computerized estimation of the outcome of the ischemic stroke lesion can assist physicians in better evaluating the stroke and providing information on tissue outcomes. This can be achieved by accurately classifying the characteristics employing a convolutional neural network with convolutional layers. Segmentation calls for retaining structural characteristics of pixels in the process of learning the local properties of a picture. Therefore, in the present investigation, a deep learning network is used to segment the Ischemia. The key finding of this study is the extraction of ischemic lesion features through the deployment of the InceptionV3 network and the preservation of information on the z axis through the use of a traditional 3D-U-Net architecture. The trials conducted on the ISLES 2017 dataset yielded an overall segmentation dice coefficient of 0.43. The findings of this study demonstrate that our proposed methodology surpasses previous research.