title: Context-specific selection of algorithms for recursive feature tracking in endoscopic image using a new methodology.
abstract: In minimally invasive surgery, the tracking of deformable tissue is a critical component for image-guided applications. Deformation of the tissue can be recovered by tracking features using tissue surface information (texture, color,...). Recent work in this field has shown success in acquiring tissue motion. However, the performance evaluation of detection and tracking algorithms on such images are still difficult and are not standardized. This is mainly due to the lack of ground truth data on real data. Moreover, in order to avoid supplementary techniques to remove outliers, no quantitative work has been undertaken to evaluate the benefit of a pre-process based on image filtering, which can improve feature tracking robustness. In this paper, we propose a methodology to validate detection and feature tracking algorithms, using a trick based on forward-backward tracking that provides an artificial ground truth data. We describe a clear and complete methodology to evaluate and compare different detection and tracking algorithms. In addition, we extend our framework to propose a strategy to identify the best combinations from a set of detector, tracker and pre-process algorithms, according to the live intra-operative data. Experimental results have been performed on in vivo datasets and show that pre-process can have a strong influence on tracking performance and that our strategy to find the best combinations is relevant for a reasonable computation cost.
authors.affiliation: Biomedical Engineering Laboratory, Sciences Engineering Faculty, Abou Bekr Belkaid University, Tlemcen, Algeria; Research Institute against Digestive Cancer, IRCAD 1 place de l'Hopital, Strasbourg, France. Electronic address: selka.faical@gmail.com.
authors.forename: F
authors.initials: F
authors.lastname: Selka
authors.affiliation: Research Institute against Digestive Cancer, IRCAD 1 place de l'Hopital, Strasbourg, France. Electronic address: stephane.nicolau@ircad.fr.
authors.forename: S
authors.initials: S
authors.lastname: Nicolau
authors.affiliation: Research Institute against Digestive Cancer, IRCAD 1 place de l'Hopital, Strasbourg, France. Electronic address: vincent.agnus@ircad.fr.
authors.forename: V
authors.initials: V
authors.lastname: Agnus
authors.affiliation: Biomedical Engineering Laboratory, Sciences Engineering Faculty, Abou Bekr Belkaid University, Tlemcen, Algeria. Electronic address: a.bessaid@gmail.com.
authors.forename: A
authors.initials: A
authors.lastname: Bessaid
authors.affiliation: Research Institute against Digestive Cancer, IRCAD 1 place de l'Hopital, Strasbourg, France; IHU 1 place de l'Hopital, Strasbourg, France. Electronic address: jacques.marescaux@ircad.fr.
authors.forename: J
authors.initials: J
authors.lastname: Marescaux
authors.affiliation: Research Institute against Digestive Cancer, IRCAD 1 place de l'Hopital, Strasbourg, France; IHU 1 place de l'Hopital, Strasbourg, France. Electronic address: luc.soler@ircad.fr.
authors.forename: L
authors.initials: L
authors.lastname: Soler
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016145:Endoscopy, Digestive System / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D025321:Surgery, Computer-Assisted / Q000379:methods*
keywords: Artificial ground truth; Context-specific selection; Feature tracking; Framework validation; Minimally invasive surgery; Pre-process