Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets
share
Cerebrovascular accident or stroke, is an acute disease with extreme impact on patients and healthcare systems and is the second largest cause of death worldwide. Fast and precise stroke lesion detection and location is an extreme important process with regards to stroke diagnosis, treatment, and prognosis. Except from the manual segmentation and traditional segmentation approach, machine learning based segmentation methods are the most promising ones when considering efficiency and accuracy, and convolutional neural network based models are the first of its kind. However, most of these neural network models do not really align with the brain anatomical structures. Intuitively, this work presents a more brain alike model which mimics the anatomical structure of the human visual cortex. Through the preliminary experiments on stroke lesion segmentation task, the proposed model is found to be able to perform equally well to some of the state-of-the-art models.
READ FULL TEXT
