BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Abstract

Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high-and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration Hilbert et al. Fully-Automated Arterial Brain Vessel Segmentation of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases.

A precision medicine framework for personalized simulation of hemodynamics in cerebrovascular disease

“…With the advent of powerful machine learning segmentation methods in recent years, it is very likely that these steps can be automated with sufficient performance. For segmentation, our group has just recently presented deep learning methods to segment the vasculature from structural scans with very high accuracy [25,34]. The application of deep learning for skeletonization and automated annotation is a current focus of our group.…”

A Precision Medicine Framework for Personalized Simulation of Hemodynamics in Cerebrovascular Disease

“…For segmentation, our group has just recently presented deep learning methods to segment the vasculature from structural scans with very high accuracy. 25,34 The application of deep learning for skeletonization and automated annotation is a current focus of our group. We are thus confident that for future potential applications in the clinical setting simulation results will be obtainable in real-time, at the scanner console, in a few years.…”

An evaluation of performance measures for arterial brain vessel segmentation

“…The created errors are selected based on the experience of our group developing and optimising vessel segmentation algorithms. These errors were regularly encountered in segmentations produced by state of the art deep learning models [ 5 , 8 ] and also other traditional methods like region growing or graph cut algorithms [ 8 ]. Additionally, these errors are also encountered in the literature [ 21 – 25 ].…”

“…Recently, advances in deep neural network architectures, a particular type of artificial intelligence (AI), made fully automated and clinically applicable cerebral vessel segmentation approaches feasible [ 5 – 7 ]. Once deployed, these methods do not rely on human intervention and can provide high-quality binary segmentations of the arterial vessels in less than a minute [ 5 ]. However, a severe obstacle to developing and validating improved vessel segmentation approaches is accurate segmentation performance assessment.…”