Topology-aware multiclass segmentation of the Circle of Willis from MRA and CTA images

Abstract

The Circle of Willis (CoW) is an essential network of arteries that ensures blood flow throughout the brain. From a clinical perspective, evaluating the vessels of the CoW is highly relevant as its angioarchitecture and variants are important biomarkers of neurovascular pathologies. However, achieving a topologically accurate segmentation of these vessels remains challenging due to their anatomical complexity. In this work, we propose a pipeline for the multiclass segmentation of the CoW vessels (13 possible classes), focusing on achieving both topology correctness and segmentation accuracy in magnetic resonance angiography (MRA) and computed tomography angiography (CTA) imaging techniques. We propose a deep learning framework based on the nnUNet model, together with a post-processing block that requires no additional training and that is adapted to the specific multiclass CoW segmentation task. We train and validate our framework using the publicly available TopCoW 2024 dataset (MRA and CTA) and evaluate it on the hidden test set (through an online system) and on an independent subset from the CROWN 2023 challenge dataset (MRA). The obtained results demonstrate the positive impact of our approach, achieving an average Dice (centerline Dice) scores of 0.90 (0.99) for MRA and 0.88 (0.99) for CTA on the in-domain test set, and 0.81 (0.97) on the out-of-domain test set for MRA. These high performances align with state-of-the-art methods, and rank among the top in the TopCoW 2024 challenge. The approach is publicly available for the research community at https://github.com/NIC-VICOROB/CoW-multiclass-segmentation-TopCoW24

Rachika E. Hamadache holds an IFUdG2024 grant from Universitat de Girona. Clara Lisazo holds an FI grant from the Catalan Government with reference number 2024 FI-1 00103. Cansu Yalcın holds an FI grant from the Catalan Government with reference number 2023 FI-1 00096. Uma M. Lal-Trehan Estrada holds an IFUdG2022 grant from Universitat de Girona. Valeriia Abramova holds an FPI grant from the Ministerio de Ciencia, Innovación y Universidades with reference number PRE2021-099121. Adrià Casamitjana holds a Ramón y Cajal grant from the Spanish Government with reference number RYC2024-050753-I. This work has been supported by PID2023-146187OB-I00 from the Ministerio de Ciencia, Innovación y Universidades and also by the ICREA Academia program. Open Access funding was provided thanks to the CRUE-CSIC agreement with Elsevier