Guide-wire extraction through perceptual organization of local segments in fluoroscopic images.
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.
Honnorat
Nicolas
N
Laboratoire MAS, Ecole Centrale Paris, Châtenay-Malabry, France.
Vaillant
Régis
R
Paragios
Nikos
N
D000465:Algorithms; D017130:Angioplasty / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015641:Radiography, Interventional / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*