Coronary artery centerline tracking with the Morphological Skeleton Loss

Mario Viti (1, 2) , Hugues Talbot (1, 3) , Bassam Abdallah (2) , Etienne Perot (4, 2) , Nicolas Gogin (2)

1 OPIS - OPtimisation Imagerie et Santé
2 GE Healthcare - General Electric Medical Systems [Buc]
3 CVN - Centre de vision numérique
4 Valeo Driving Assistance Domain

Coronary computed tomography angiography (CCTA) provides a non-invasive imaging solution that reliably depicts the anatomy of coronary arteries. Diagnosing coronary artery diseases (CAD) entails a clinical evaluation of stenosis and plaques, which is in turn essential for obtaining a reliable coronary-artery centerline from CCTA 3D imaging. This work proposes a centerline extraction algorithm by combining local semantic segmentation and recursive tracking. To this end we propose a Morphological Skeleton Loss (MS Loss) suited for 3D centerline segmentation based on an improved morphological skeleton algorithm coupled with a resource-efficient back-propagation scheme. This work employs 225 CCTA examinations paired with manually annotated coronary-artery centerlines. This method is compared against the deep-learning state of the art in the literature using a standardized evaluation method for coronary-artery tracking.

Mots clés: CT angiography; segmentation; blood vessels; deep learning; Connectivity

Domaines: Informatique [cs]

Mario Viti, Hugues Talbot, Bassam Abdallah, Etienne Perot, Nicolas Gogin. Coronary artery centerline tracking with the Morphological Skeleton Loss. IEEE International Conference on Image Processing (ICIP), Oct 2018, Bordeaux, France. ⟨10.1109/ICIP46576.2022.9897385⟩. ⟨hal-03724882⟩