Topology-Aware Exploration of Circle of Willis for CTA and MRA: Segmentation, Detection, and Classification
This work addresses the need for improved segmentation, detection, and classification of brain vasculature for clinical evaluation of neuro-vascular diseases, representing an incremental advancement in medical imaging analysis.
The paper tackled the problem of analyzing the Circle of Willis (CoW) vessels from CTA and MRA images using a unified framework with topology-aware methods, achieving competitive results including first place in the CTA-Edg-Task and second place in multiple tasks in the TopCow24 Challenge.
The Circle of Willis (CoW) vessels is critical to connecting major circulations of the brain. The topology of the vascular structure is clinical significance to evaluate the risk, severity of the neuro-vascular diseases. The CoW has two representative angiographic imaging modalities, computed tomography angiography (CTA) and magnetic resonance angiography (MRA). TopCow24 provided 125 paired CTA-MRA dataset for the analysis of CoW. To explore both CTA and MRA images in a unified framework to learn the inherent topology of Cow, we construct the universal dataset via independent intensity preprocess, followed by joint resampling and normarlization. Then, we utilize the topology-aware loss to enhance the topology completeness of the CoW and the discrimination between different classes. A complementary topology-aware refinement is further conducted to enhance the connectivity within the same class. Our method was evaluated on all the three tasks and two modalities, achieving competitive results. In the final test phase of TopCow24 Challenge, we achieved the second place in the CTA-Seg-Task, the third palce in the CTA-Box-Task, the first place in the CTA-Edg-Task, the second place in the MRA-Seg-Task, the third palce in the MRA-Box-Task, the second place in the MRA-Edg-Task.