Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
This work addresses the problem of tracking disease evolution in Barrett's esophagus for medical professionals, representing an incremental improvement over prior methods.
The paper tackles the challenge of re-localizing biopsied sites in endoscopic surveillance by extending an inter-operative relocalization framework to automatically select the best viewpoint match, achieving retrieval rates of 92% and 87% for NBI and WL modalities, up from 73% and 76% in previous work.
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.