eprintid: 1450866 rev_number: 34 eprint_status: archive userid: 608 dir: disk0/01/45/08/66 datestamp: 2014-10-08 19:18:02 lastmod: 2021-09-23 22:24:09 status_changed: 2018-07-31 14:09:32 type: proceedings_section metadata_visibility: show item_issues_count: 0 creators_name: Tafreshi, MK creators_name: Linard, N creators_name: André, B creators_name: Ayache, N creators_name: Vercauteren, T title: Semi-automated query construction for content-based endomicroscopy video retrieval ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F42 keywords: Algorithms, Colonic Polyps, Colonoscopy, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Microscopy, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface, Scale Invariant Feature Transform, Video Retrieval, Confocal Laser Endomicroscopy, Temporal Segmentation, Scale Invariant Feature Transform Descriptor note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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. date: 2014-09-14 publisher: Springer, Cham official_url: https://doi.org/10.1007/978-3-319-10404-1_12 vfaculties: VENG oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_source: WoS-Lite elements_id: 983656 doi: 10.1007/978-3-319-10404-1_12 lyricists_name: Vercauteren, Tom lyricists_id: TVERC65 full_text_status: public series: Lecture Notes in Computer Science publication: Med Image Comput Comput Assist Interv volume: 17 number: Pt 1 place_of_pub: Boston, MA, USA pagerange: 89-96 event_title: 17th International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2014 event_location: Germany book_title: Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2014 citation: Tafreshi, MK; Linard, N; André, B; Ayache, N; Vercauteren, T; (2014) Semi-automated query construction for content-based endomicroscopy video retrieval. In: Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2014. (pp. pp. 89-96). Springer, Cham: Boston, MA, USA. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1450866/1/Vercauteren_paper-841.pdf