TY - GEN
T1 - First steps into endoscopic video analysis for Barrett's cancer detection: Challenges and opportunities
AU - van der Putten, Joost
AU - de Groof, Jeroen
AU - van der Sommen, Fons
AU - Struyvenberg, Maarten
AU - Zinger, Svitlana
AU - Curvers, Wouter
AU - Schoon, Erik
AU - Bergman, Jacques
AU - de With, Peter H. N.
PY - 2020
Y1 - 2020
N2 - Routine surveillance endoscopies are currently used to detect dysplasia in patient with Barrett's Esophagus (BE). However, most of these procedures are performed by non-expert endoscopists in community hospitals. Leading to many missed dysplastic lesions, which can progress into advanced esophageal adenocarcinoma if left untreated.1 In recent years, several successful algorithms have been proposed for the detection of cancer in BE using high-quality overview images. This work addresses the first steps towards clinical application on endoscopic surveillance videos. Several challenges are identified that occur when moving from image-based to video-based analysis. (1) It is shown that algorithms trained on high-quality overview images do not naively transfer to endoscopic videos due to e.g. non-informative frames. (2) Video quality is shown to be an important factor in algorithm performance. Specifically, temporal location performance is highly correlated with video quality. (3) When moving to real-time algorithms, the additional compute necessary to address the challenges in videos will become a burden on the computational budget. However, in addition to challenges, videos also bring new opportunities not available in the current image-based methods such as the inclusion of temporal information. This work shows that a multi-frame approach increases performance compared to a naive single-image method when the above challenges are addressed.
AB - Routine surveillance endoscopies are currently used to detect dysplasia in patient with Barrett's Esophagus (BE). However, most of these procedures are performed by non-expert endoscopists in community hospitals. Leading to many missed dysplastic lesions, which can progress into advanced esophageal adenocarcinoma if left untreated.1 In recent years, several successful algorithms have been proposed for the detection of cancer in BE using high-quality overview images. This work addresses the first steps towards clinical application on endoscopic surveillance videos. Several challenges are identified that occur when moving from image-based to video-based analysis. (1) It is shown that algorithms trained on high-quality overview images do not naively transfer to endoscopic videos due to e.g. non-informative frames. (2) Video quality is shown to be an important factor in algorithm performance. Specifically, temporal location performance is highly correlated with video quality. (3) When moving to real-time algorithms, the additional compute necessary to address the challenges in videos will become a burden on the computational budget. However, in addition to challenges, videos also bring new opportunities not available in the current image-based methods such as the inclusion of temporal information. This work shows that a multi-frame approach increases performance compared to a naive single-image method when the above challenges are addressed.
KW - barrett's esophagus
KW - multi-frame classication
KW - real-time
KW - temporal localization
UR - http://www.scopus.com/inward/record.url?scp=85085527279&partnerID=8YFLogxK
U2 - https://doi.org/10.1117/12.2544229
DO - https://doi.org/10.1117/12.2544229
M3 - Conference contribution
VL - 11314
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Hahn, Horst K.
A2 - Mazurowski, Maciej A.
PB - SPIE
T2 - Medical Imaging 2020: Computer-Aided Diagnosis
Y2 - 16 February 2020 through 19 February 2020
ER -