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

Abstract

Automated vessel segmentation in cerebral digital subtraction angiography (DSA) has significant clinical utility in the management of cerebrovascular diseases. Although deep learning has become the foundation for state-of-the-art image segmentation, a significant amount of labeled data is needed for training. Furthermore, due to domain differences, pre-trained networks cannot be applied to DSA data out-of-the-box. To address this, 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 when utilizing weak supervision strategies. For comparison purposes, we also demonstrate our approach on the Digital Retinal Images for Vessel Extraction (DRIVE) dataset. 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.