Thrombus detection in CT brain scans using a convolutional neural network

Automatic detection and measurement of thrombi may expedite clinical workflow in the treatment planning stage. Nevertheless it is a challenging task on non-contrast computed tomography due to the subtlety of the pathological intensity changes, which are further confounded by the appearance of vascular calcification (common in ageing brains). In this paper we propose a 3D Convolutional Neural Network architecture to detect these subtle signs of stroke. The architecture is designed to exploit contralateral features and anatomical atlas information. We use 122 CT volumes equally split into training and testing to validate our method, achieving a ROC AUC of 0.996 and a Precision-Recall AUC of 0.563 in a voxel-level evaluation. The results are not yet at a level for routine clinical use, but they are encouraging.