Unsupervised segmentation of stents corrupted by artifacts in medical X-ray images

Hugo Gangloff (1, 2) , Emmanuel Monfrini (3, 4, 5) , Christophe Collet (1) , Nabil Chakfe (6, 2)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 Groupe Européen de Recherche sur les Prothèses appliquées à la Chirurgie Vasculaire, Strasbourg
3 IP Paris - Institut Polytechnique de Paris
4 TSP - CITI - Communications, Images et Traitement de l'Information
5 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications
6 HUS - Les Hôpitaux Universitaires de Strasbourg

We propose a new methodology for the segmentation of stents in 3D X-ray acquisitions. Such data are often corrupted by strong artifacts around the stent, requiring the development of a robust algorithm: because of the medical application, we need to produce an accurate segmentation. Moreover, we aim at developping a robust technique that can handle heterogeneous data. We propose a two-step, coarse-to-fine approach, that handles the corrupted cases. This approach leads to better results illustrated in the context of metallic artefact reduction.

Mots clés: Image segmentation; Probabilistic and statisticalmodels; Metal artifacts; X-ray imaging

Domaines: Traitement du signal et de l'image [eess.SP]; Applications [stat.AP]

Hugo Gangloff, Emmanuel Monfrini, Christophe Collet, Nabil Chakfe. Unsupervised segmentation of stents corrupted by artifacts in medical X-ray images. IPTA 2020: 10th international conference on Image Processing Theory, Tools and Applications, Nov 2020, Paris, France. pp.1-6, ⟨10.1109/IPTA50016.2020.9286660⟩. ⟨hal-03122418⟩