Semi-automated Query Construction for Content-Based Endomicroscopy Video Retrieval

Abstract

Abstract. Content-based video retrieval has shown promising results to help physicians in their interpretation of medical videos in general and endomicroscopic ones in particular. Defining a relevant query for CBVR can however be a complex and time-consuming task for non-expert and even expert users. Indeed, uncut endomicroscopy videos may very well contain images corresponding to a variety of different tissue types. Using such uncut videos as queries may lead to drastic performance degradations for the system. In this study, we propose a semi-automated methodology that allows the physician to create meaningful and relevant queries in a simple and efficient manner. We believe that this will lead to more reproducible and more consistent results. The validation of our method is divided into two approaches. The first one is an indirect validation based on per video classification results with histopathological ground-truth. The second one is more direct and relies on perceived inter-video visual similarity ground-truth. We demonstrate that our proposed method significantly outperforms the approach with uncut videos and approaches the performance of a tedious manual query construction by an expert. Finally, we show that the similarity perceived between videos by experts is significantly correlated with the inter-video similarity distance computed by our retrieval system.

Image computing for fibre-bundle endomicroscopy: A review

“…The feedback was combined on Isomap dimensionality reduction for improved performance and efficiency. (Tous et al, 2012) In an attempt to improve the retrieval performance of (André et al, 2011b), (Kohandani Tafresh et al, 2014) introduced a simple and efficient semi-automated approach allowing clinicians to create more meaningful queries than unprocessed endomicroscopic video sequences. The approach automatically temporally segmented endomicroscopic video sequence based on kinematic stability assessment, with informative sub-segments assumed spatially stable.…”