RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

Hrvoje Bogunović, Freerk G. Venhuizen, Sophie Riedl, Stefanos Apostolopoulos, Alireza Bab‐Hadiashar, Ulaş Bağcı, Mirza Faisal Beg, Loza Bekalo et al.

IEEE Transactions on Medical Imaging 2019 article cited by 273

Source More in Medicine & Health

importance 6/10 partially solved Retinal Imaging and Analysis retinal imaging stage: research

Problem

There was no standardized way to judge how well AI methods detect retinal fluid in OCT scans.

Why it matters

Retinal fluid causes vision-threatening disease and OCT is the standard of care for monitoring it.

What the AI did

A MICCAI 2017 challenge had eight teams apply deep learning to fluid detection and segmentation on annotated OCT images from two clinical centers and three scanner vendors.

Results

not stated in source

Impact

The benchmark provided the first standardized multi-vendor, multi-disease test set for retinal fluid AI.

Record

Venue	IEEE Transactions on Medical Imaging
Published	2019
Work type	article
Authors	Hrvoje Bogunović, Freerk G. Venhuizen, Sophie Riedl, Stefanos Apostolopoulos, Alireza Bab‐Hadiashar, Ulaş Bağcı, Mirza Faisal Beg, Loza Bekalo et al.
Citations	273 (OpenAlex, at import time)
References	81
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1109/tmi.2019.2901398
Open access	closed
Keywords	retinal OCT, fluid segmentation, benchmark, deep learning, MICCAI

Importance rating

6 / 10

significant — thousands to millions affected

Abstract states retinal fluid accumulation is associated with the most vision-threatening retinal diseases and OCT is the current standard of care; benchmark enables standardized evaluation of AI for this clinical task.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

More in Medicine & Health

#	Paper	Year	Citations	Importance
1	Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction Yeong Chan Lee, Jiho Cha, Injeong Shim, Woong‐Yang Park, Se Woong Kang...	2023	107	10
2	Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman, Amith Abdullah Khandakar, Muhammad Abdul Kadir, Khand...	2020	651	10
3	A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, Sa...	2022	116	10
4	Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Mu...	2022	89	10
5	Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, Arup Acharjee...	2021	76	10