Weakly-Supervised Convolutional Neural Networks for Vessel Segmentation in Cerebral Angiography

Automated vessel segmentation in cerebral digital subtraction angiography (DSA) has significant clinical utility in the management of cerebrovascular diseases such as stroke diagnosis and detection of aneurysms. While deep learning is state-of-the-art in segmentation, a significant amount of labeled data is needed for training. Because of domain differences, pretrained networks cannot be applied to DSA data out-of-the-box. We propose a novel learning framework, which utilizes an active contour model for weak supervision and low-cost human-in-the-loop strategies to improve weak label quality. Our study produces several significant results, including state-of-the-art results for cerebral DSA vessel segmentation, which exceed human annotator quality, and an analysis of annotation cost and model performance trade-offs utilizing weak supervision strategies. Additionally, we will be publicly releasing code to reproduce our methodology and our dataset, the largest known high-quality annotated cerebral DSA vessel segmentation dataset.