Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance

Anant S. Vemuri (1, 2) , Stéphane Nicolau (3) , Jacques Marescaux (3) , Luc Soler (2, 3) , Nicholas Ayache (1)

1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
2 IHU Strasbourg - L'Institut hospitalo-universitaire de Strasbourg
3 IRCAD/EITS - Institut de Recherche Contre les Cancers de l'Appareil Digestif-European Institute of Telesurgery

Abstract. Esophageal adenocarcinoma arises from Barrett’s esophagus, which is the most serious complication of gastroesophageal reflux disease. Strategies for screening involve periodic surveillance and tissue biopsies. A major challenge in such regular examinations is to record and track the disease evolution and re-localization of biopsied sites to provide targeted treatments. In this paper, we extend our original inter-operative relocalization framework to provide a constrained image based search for obtaining the best view-point match to the live view. Within this context we investigate the effect of, (a) the choice of feature descriptors and color-space, (b) filtering of uninformative frames, (c) endoscopic modality, for view-point localization. Our experiments indicate an improvement in the best view-point retrieval rate to [92%, 87%] from [73%, 76%] (in our previous approach) for NBI and WL.

Mots clés: Computer assisted intervention; Endoscopic Image classification; Endoluminal surgery; Barrett's Oesophagus

Domaines: Imagerie médicale; Machine Learning [stat.ML]

Anant S. Vemuri, Stéphane Nicolau, Jacques Marescaux, Luc Soler, Nicholas Ayache. Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance . Medical Content-based Retrieval for Clinical Decision Support, Tanveer Syeda-Mahmood; Hayit Greenspan; Anant Madabhushi, Oct 2015, Munich, Germany. pp.1-8. ⟨hal-01203463⟩