@inproceedings{23dae0c74f6b4779b437a60fcf3015eb,
title = "Unsupervised segmentation of stents corrupted by artifacts in medical X-ray images",
abstract = "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.",
keywords = "X-ray imaging, image segmentation, metal artifacts, probabilistic and statistical models",
author = "Hugo Gangloff and Emmanuel Monfrini and Christophe Collet and Nabil Chakfe",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020 ; Conference date: 09-11-2020 Through 12-11-2020",
year = "2020",
month = nov,
day = "9",
doi = "10.1109/IPTA50016.2020.9286660",
language = "English",
series = "2020 10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020",
}