Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions

Perfusion CT is widely used in acute ischemic stroke to determine eligibility for acute treatment, by defining an ischemic core and penumbra. In this work, we propose a novel way of building on prior information for the automatic prediction and segmentation of stroke lesions. To this end, we reformulate the task to identify differences from a prior segmentation by extending a three-dimensional Attention Gated Unet with a skip connection allowing only an unchanged prior to bypass most of the network. We show that this technique improves results obtained by a baseline Attention Gated Unet on both the Geneva Stroke Dataset and the ISLES 2018 dataset.