Learning‐based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery

Abstract

We developed a novel, deeply supervised, deep learning-based approach to automatically segment the AVM volume on CT images. We demonstrated its clinical feasibility by validating the shape and positional accuracy, and dose coverage of the automatic volume. These results demonstrate the potential of a learning-based segmentation method for delineating AVMs in the clinical setting.