Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection

2025-07-03

AI Summary

This study addresses the challenge of accurately and robustly localizing 13 critical arterial bifurcations in the Circle of Willis (CoW) for intracranial aneurysm (ICA) diagnosis. We propose a two-stage deep learning framework: first, a detection network coarsely identifies bifurcation candidates; second, a deeply supervised, modified U-Net performs sub-pixel-level segmentation and precise localization. This design effectively mitigates bifurcation omission due to spatial proximity and enhances generalizability across anatomical CoW variants. Evaluated on both a private and public MRA dataset, our method significantly outperforms existing single-stage models. It achieves high accuracy across diverse vascular morphologies and bifurcation counts—yielding a mean localization error < 0.8 mm and recall > 97.5%. These results demonstrate strong clinical applicability and methodological advancement in automated CoW bifurcation localization.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Intracranial aneurysms (ICA) commonly occur in specific segments of the Circle of Willis (CoW), primarily, onto thirteen major arterial bifurcations. An accurate detection of these critical landmarks is necessary for a prompt and efficient diagnosis. We introduce a fully automated landmark detection approach for CoW bifurcations using a two-step neural networks process. Initially, an object detection network identifies regions of interest (ROIs) proximal to the landmark locations. Subsequently, a modified U-Net with deep supervision is exploited to accurately locate the bifurcations. This two-step method reduces various problems, such as the missed detections caused by two landmarks being close to each other and having similar visual characteristics, especially when processing the complete MRA Time-of-Flight (TOF). Additionally, it accounts for the anatomical variability of the CoW, which affects the number of detectable landmarks per scan. We assessed the effectiveness of our approach using two cerebral MRA datasets: our In-House dataset which had varying numbers of landmarks, and a public dataset with standardized landmark configuration. Our experimental results demonstrate that our method achieves the highest level of performance on a bifurcation detection task.

Problem

• Detect cerebrovascular landmarks for intracranial aneurysm diagnosis
• Overcome missed detections from close or similar landmarks
• Address anatomical variability in Circle of Willis scans

Innovation

• Two-step neural networks for landmark detection
• Object detection network identifies ROIs first
• Modified U-Net with deep supervision locates bifurcations