Automatic Brain Arteriovenous Malformations Segmentation on Contrast CT Images Using Combined Region Proposal Network and V-Net

Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time-consuming and subject to inter- and intra-observer variation. Therefore, it is important to develop an automatic segmentation method to delineate the AVM target from CT images. In this study, we retrospectively investigated 80 patients who were treated with SRS. Ground truth was manually generated by an experienced physician using both DSA and CT images. A fast region proposal network was first trained to propose a bounding box that contains the AVM lesion for detection. The bounding box was then used to guide image patch sampling process for V-Net training. In the testing stage, possible AVM locations were first proposed by the region proposal network. Subsequently, V-Net was used for the final label prediction. Both the region proposal network and V-Net were trained using 60 patients and tested using 20 patients. The mean Dice similarity coefficient (DSC) was calculated to evaluate the accuracy of the proposed method. The automatic contours were in very good agreement to the ground truth contours with an average DSC > 0.85.