ST4 - Computer-aided and robotic endoscopy systems

Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Improved Barrett's Cancer Detection in Volumetric Laser Endomicroscopy scans using Multiple-Frame Voting
This paper explores the feasibility of using multi-frame analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Barett's Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers considerable advantages compared to current methods.
Alexandros Rikos's picture
Alexandros Rikos
Fons van der Sommen's picture
Fons van der Sommen
Eindhoven Univeristy of Technology (NL)
Anne-Fre Swager's picture
Anne-Fre Swager
Svitlana Zinger's picture
Svitlana Zinger
Wouter Curvers's picture
Wouter Curvers
Erik Schoon's picture
Erik Schoon
Jacques Bergman's picture
Jacques Bergman
Peter de With's picture
Peter de With
Real-Time Instrument Scene Detection in Screening GI Endoscopic Procedures
Here we describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to create training data: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts.
Chuanhai Zhang's picture
Chuanhai Zhang
Iowa State University (USA)
Wallapak Tavanapong's picture
Wallapak Tavanapong
Johnny Wong's picture
Johnny Wong
Piet C. de Groen's picture
Piet C. de Groen
Junghwan Oh's picture
Junghwan Oh
Sparse coded handcrafted and deep features for colon capsule video summarization
Abstract—Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45–90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space.
Ahmed Mohammed's picture
Ahmed Mohammed
Norwegian University of Science and Technology (NO)
Sule Yildirim's picture
Sule Yildirim
Marius Pedersen's picture
Marius Pedersen
Oistein Hovde's picture
Oistein Hovde
Faouzi Cheikh's picture
Faouzi Cheikh
Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks
Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non- invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation.
George Dimas's picture
George Dimas
Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Alexandros Karargyris's picture
Alexandros Karargyris
Gastone Ciuti's picture
Gastone Ciuti
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Retinal OCT Image Segmentation Using Fuzzy Histogram Hyperbolization and Continuous Max-Flow
The segmentation of retinal layers is vital for tracking progress of medication and diagnosis of various eye diseases. To date many methods for the analysis exists, however the speckle noise and shadows of retinal blood vessel remains a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts have been focused on image pre-processing or developing sophisticated models for segmentation to address this problem, but it still remains an area of active research.
Bashir Dodo's picture
Bashir Dodo
Brunel Unevirsity London (UK)
Yongmin Li's picture
Yongmin Li
Xiaohui Liu's picture
Xiaohui Liu