Computational Retinal Image Analysis

Tools, Applications and Perspectives

1st Edition - November 19, 2019
Editors: Emanuele Trucco, Tom MacGillivray, Yanwu Xu
Language: English
Paperback ISBN:
9 7 8 - 0 - 0 8 - 1 0 2 8 1 6 - 2
eBook ISBN:
9 7 8 - 0 - 0 8 - 1 0 2 8 1 7 - 9

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informati… Read more

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Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.

CHAPTER 1 A brief introduction and a glimpse into the past
Emanuele Trucco, Yanwu Xu, and Tom MacGillivray

CHAPTER 2 Clinical motivation and the needs for RIA
in healthcare
Ryo Kawasaki and Jakob Grauslund

CHAPTER 3 The physics, instruments and modalities
of retinal imaging
Andrew R. Harvey, Guillem Carles, Adrian Bradu and
Adrian Podoleanu

CHAPTER 4 Retinal image preprocessing, enhancement,
and registration
Carlos Hernandez-Matas, Antonis A. Argyros
and Xenophon Zabulis

CHAPTER 5 Automatic landmark detection in fundus
photography
Jeffrey Wigdahl, Pedro Guimarães and Alfredo Ruggeri

CHAPTER 6 Retinal vascular analysis: Segmentation,
tracing, and beyond
Li Cheng, Xingzheng Lyu, He Zhao, Huazhu Fu
and Huiqi Li

CHAPTER 7 OCT layer segmentation
Sandro De Zanet, Carlos Ciller, Stefanos Apostolopoulos,
Sebastian Wolf and Raphael Sznitman

CHAPTER 8 Image quality assessment
Sarah A. Barman, Roshan A. Welikala, Alicja R. Rudnicka
and Christopher G. Owen

CHAPTER 9 Validation
Emanuele Trucco, Andrew McNeil, Sarah McGrory, Lucia
Ballerini, Muthu Rama Krishnan Mookiah, Stephen Hogg,
Alexander Doney and Tom MacGillivray

CHAPTER 10 Statistical analysis and design in
ophthalmology: Toward optimizing your data
Gabriela Czanner and Catey Bunce

CHAPTER 11 Structure-preserving guided retinal
image filtering for optic disc analysis
Jun Cheng, Zhengguo Li, Zaiwang Gu, Huazhu Fu,
Damon Wing Kee Wong and Jiang Liu

CHAPTER 12 Diabetic retinopathy and maculopathy lesions
Bashir Al-Diri, Francesco Calivá, Piotr Chudzik,
Giovanni Ometto and Maged Habib

CHAPTER 13 Drusen and macular degeneration
Bryan M. Williams, Philip I. Burgess and
Yalin Zheng

CHAPTER 14 OCT fluid detection and quantification
Hrvoje Bogunović, Wolf-Dieter Vogl,
Sebastian M. Waldstein and Ursula Schmidt-Erfurth

CHAPTER 15 Retinal biomarkers and cardiovascular
disease: A clinical perspective
Carol Yim-lui Cheung, Posey Po-yin Wong and
Tien Yin Wong

CHAPTER 16 Vascular biomarkers for diabetes
and diabetic retinopathy screening
Fan Huang, Samaneh Abbasi-Sureshjani, Jiong Zhang,
Erik J. Bekkers, Behdad Dashtbozorg and
Bart M. ter Haar Romeny

CHAPTER 17 Image analysis tools for assessment of atrophic
macular diseases
Zhihong Jewel Hu and Srinivas Reddy Sadda

CHAPTER 18 Artificial intelligence and deep learning
in retinal image analysis
Philippe Burlina, Adrian Galdran, Pedro Costa, Adam
Cohen and Aurélio Campilho

CHAPTER 19 AI and retinal image analysis at Baidu
Yehui Yang, Dalu Yang, Yanwu Xu, Lei Wang,
Yan Huang, Xing Li, Xuan Liu and Le Van La

CHAPTER 20 The challenges of assembling, maintaining
and making available large data sets
of clinical data for research
Emily R. Jefferson and Emanuele Trucco

CHAPTER 21 Technical and clinical challenges
of A.I. in retinal image analysis
Gilbert Lim, Wynne Hsu, Mong Li Lee, Daniel Shu Wei
Ting and Tien Yin Wong

Emanuele Trucco

Manuel has been active since 1984 in computer vision, and since 2002 in medical image analysis. He has published more than 250 refereed papers and 2 textbooks (one of which an international standard with 2,793 citations, Google Scholar 25 Oct 2016). He is co-director of VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina), an international research initiative led by the Universities of Dundee and Edinburgh (co-director Dr T MacGillivray). VAMPIRE develops software tools for efficient data and image analysis, with a focus on multi-modal retinal images. VAMPIRE has been used in UK and international biomarker studies on cardiovascular risk, stroke, dementia, diabetes and complications, cognitive performance, neurodegenerative diseases, and genetics.

Affiliations and expertise

Chair of Computational Vision in Computing, School of Science and Engineering, University of Dundee, and Honorary Clinical Researcher, NHS Tayside

Tom MacGillivray

Dr Tom MacGillivray is an expert in the field of image processing and analysis for clinical research. His team staffs the Image Analysis Core laboratory of the Edinburgh Imaging group joint with the Edinburgh Clinical Research Facility, at the University of Edinburgh where he is a Senior Research Fellow. The laboratory provides specialist support to investigators accessing data from a variety of imaging modalities including MR, CT, PET, ultrasound and retinal imaging. Dr MacGillivray has extensive experience with retinal image processing and analysis with more than 15 years experience facilitating clinical research that features retinal imaging. This includes studies on stroke, cardiovascular disease, MS, diabetes, kidney disease, dementia and age-related cognitive change. In close collaboration with the University of Dundee (Prof E. Trucco, School of Computing), he co-ordinates an interdisciplinary initiative called VAMPIRE (Vascular Assessment and Measurement Platform for Images of the REtina, vampire.computing.dundee.ac.uk) whose aim is efficient, semi-automatic analysis of retinal images and the pursuit of biomarker identification.

Affiliations and expertise

Senior Research Fellow, University of Edinburgh, UK

Yanwu Xu

Yanwu Xu (Frank) is the Chief Architect/Scientist of AI Innovation Business Department, Baidu Online Network Technology (Beijing) Co., Ltd. He is also an Adjunct Professor at Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences (CAS). He received the B.Eng. and PhD degrees from the University of Science and Technology of China, in 2004 and 2009, respectively. He worked as a postdoctoral Research Fellow at Nanyang Technological University, Singapore, from 2009 to 2011, a Research Scientist at Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore, from 2011 to 2017, and the head of Biomedical Research Department at Central Research Institute, CVTE, from 2017 to 2018. He has published more than 100 papers in international journals and conferences, including T-MI, T-SMCB, JAMIA, MICCAI, etc. He has applied for more than 30 China patents (5 granted) and 11 PCT international patents (5 granted), including two licensed to a NMC and a Singapore startup.

Affiliations and expertise

Chief Architect/Scientist of AI Innovation Business Department, Baidu Online Network Technology (Beijing) Co., Ltd. and Adjunct Professor, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (CAS), China"