Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance

TL;DR

This paper presents an automated method for selecting optimal endoscopic viewpoints during procedures, improving tracking and localization of biopsied sites to enhance surveillance accuracy.

Contribution

It extends previous relocalization frameworks by introducing a constrained image search that optimizes viewpoint matching using various feature descriptors and filtering techniques.

Findings

Viewpoint retrieval rate improved to 92% and 87% for NBI and WL modalities.

The approach enhances tracking accuracy in endoscopic surveillance.

Filtering uninformative frames boosts localization performance.

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: the choice of feature descriptors and color-space; filtering of uninformative frames and 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.