TY - GEN
T1 - Guide-wire extraction through perceptual organization of local segments in fluoroscopic images
AU - Honnorat, Nicolas
AU - Vaillant, Régis
AU - Paragios, Nikos
N1 - Funding Information:
This work was supported by ANRT (grant 1062/2008) and GE Healthcare. The authors thank N. Komodakis for providing the clustering method.
PY - 2010
Y1 - 2010
N2 - Segmentation of surgical devices in fluoroscopic images and in particular of guide-wires is a valuable element during surgery. In cardiac angioplasty, the problem is particularly challenging due to the following reasons: (i) low signal to noise ratio, (ii) the use of 2D images that accumulate information from the whole volume, and (iii) the similarity between the structure of interest and adjacent anatomical structures. In this paper we propose a novel approach to address these challenges, that combines efficiently low-level detection using machine learning techniques, local unsupervised clustering detections and finally high-level perceptual organization of these segments towards its complete reconstruction. The latter handles miss-detections and is based on a local search algorithm. Very promising results were obtained.
AB - Segmentation of surgical devices in fluoroscopic images and in particular of guide-wires is a valuable element during surgery. In cardiac angioplasty, the problem is particularly challenging due to the following reasons: (i) low signal to noise ratio, (ii) the use of 2D images that accumulate information from the whole volume, and (iii) the similarity between the structure of interest and adjacent anatomical structures. In this paper we propose a novel approach to address these challenges, that combines efficiently low-level detection using machine learning techniques, local unsupervised clustering detections and finally high-level perceptual organization of these segments towards its complete reconstruction. The latter handles miss-detections and is based on a local search algorithm. Very promising results were obtained.
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U2 - 10.1007/978-3-642-15711-0_55
DO - 10.1007/978-3-642-15711-0_55
M3 - Conference contribution
C2 - 20879430
AN - SCOPUS:84874857108
SN - 3642157106
SN - 9783642157103
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 440
EP - 448
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Y2 - 20 September 2010 through 24 September 2010
ER -