Topology-Aware Exploration of Circle of Willis for CTA and MRA: Segmentation, Detection, and Classification

TL;DR

This paper introduces a topology-aware framework for analyzing the Circle of Willis in CTA and MRA images, improving segmentation, detection, and classification by leveraging topology-aware loss and refinement.

Contribution

It presents a unified approach combining topology-aware loss and refinement for better analysis of CoW across CTA and MRA modalities, with a new dataset and competitive results.

Findings

Achieved top rankings in multiple tasks of the TopCow24 Challenge.

Enhanced topology completeness and class discrimination in CoW analysis.

Demonstrated effectiveness across all tasks and modalities.

Abstract

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.