Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection
This work addresses the need for prompt and efficient diagnosis of intracranial aneurysms by automating landmark detection in medical imaging, though it appears incremental as it builds on existing neural network methods.
The paper tackled automated detection of cerebrovascular landmarks for intracranial aneurysm diagnosis by introducing a two-step neural network approach, achieving the highest performance on bifurcation detection tasks across two MRA datasets.
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.