title: Endoscopic video manifolds for targeted optical biopsy.
abstract: Gastro-intestinal (GI) endoscopy is a widely used clinical procedure for screening and surveillance of digestive tract diseases ranging from Barrett's Oesophagus to oesophageal cancer. Current surveillance protocol consists of periodic endoscopic examinations performed in 3-4 month intervals including expert's visual assessment and biopsies taken from suspicious tissue regions. Recent development of a new imaging technology, called probe-based confocal laser endomicroscopy (pCLE), enabled the acquisition of in vivo optical biopsies without removing any tissue sample. Besides its several advantages, i.e., noninvasiveness, real-time and in vivo feedback, optical biopsies involve a new challenge for the endoscopic expert. Due to their noninvasive nature, optical biopsies do not leave any scar on the tissue and therefore recognition of the previous optical biopsy sites in surveillance endoscopy becomes very challenging. In this work, we introduce a clustering and classification framework to facilitate retargeting previous optical biopsy sites in surveillance upper GI-endoscopies. A new representation of endoscopic videos based on manifold learning, "endoscopic video manifolds" (EVMs), is proposed. The low dimensional EVM representation is adapted to facilitate two different clustering tasks; i.e., clustering of informative frames and patient specific endoscopic segments, only by changing the similarity measure. Each step of the proposed framework is validated on three in vivo patient datasets containing 1834, 3445, and 1546 frames, corresponding to endoscopic videos of 73.36, 137.80, and 61.84 s, respectively. Improvements achieved by the introduced EVM representation are demonstrated by quantitative analysis in comparison to the original image representation and principal component analysis. Final experiments evaluating the complete framework demonstrate the feasibility of the proposed method as a promising step for assisting the endoscopic expert in retargeting the optical biopsy sites.
authors: Chair for Computer Aided Medical Procedures, Technische Universität München, München, Germany. atasoy@cs.tum.edu
authors: Selen
authors: S
authors: Atasoy
authors: Diana
authors: D
authors: Mateus
authors: Alexander
authors: A
authors: Meining
authors: Guang-Zhong
authors: GZ
authors: Yang
authors: Nassir
authors: N
authors: Navab
mesh_terms: D001185:Artificial Intelligence; D001706:Biopsy / Q000379:methods*; D016000:Cluster Analysis; D016208:Databases, Factual; D019723:Endoscopes; D016099:Endoscopy, Gastrointestinal / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D018613:Microscopy, Confocal; D025341:Principal Component Analysis; D041742:Upper Gastrointestinal Tract / Q000033:anatomy & histology / Q000473:pathology / Q000601:surgery; D014741:Video Recording / Q000379:methods*