Computational Retinal Image Analysis Tools, Applications and Perspectives The MICCAI Society book Series
Coordonnateurs : Trucco Emanuele, MacGillivray Tom, Xu Yanwu
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 Bogunovic, 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
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.
Yanwu Xu (Frank) is the Chief Architect/Scientist of AI I
- Provides a unique, well-structured and integrated overview of retinal image analysis
- Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care
- Includes plans and aspirations of companies and professional bodies
Date de parution : 11-2019
Ouvrage de 502 p.
19x23.3 cm
Thèmes de Computational Retinal Image Analysis :
Mots-clés :
Age-related macular degeneration; AMD; AMD risk factors; Annotations; Anti-VEGF; Arterial and venous vessel classification; Artificial intelligence; Atrophic macular diseases; Automated; Automated diagnostics; Automated screening; Biomarker; Brain-inspired computing; Cardiovascular disease; Cardiovascular mortality; Classification; Clinical decision support; Clinical images; Clinical need; Color fundus; Computer aided diagnosis; Computer-aided diagnosis; Convolutional neural networks; Coronary heart disease; Data governance; Deep convolutional neural networks; Deep learning; Design; Detection; Diabetic retinopathy; Diabetic retinopathy screening; Drusen; Epidemiology; Evaluation metrics; FA; Fovea; Fractal dimension; Fundus camera; Fundus photography; GDPR; Geographic atrophy; Glaucoma; Gold standard; Ground truth; History of eye research; History of vision research; Image analysis tools; Image classification; Image processing; Image quality assessment; Image segmentation; Landmark detection; Lesion detection; Lesion segmentation; Lesion spatial distribution; Level set; Light diffusion; Likelihood; Machine learning; Maculopathy lesions; Medical image analysis; Medical imaging; Microaneurysms; Missing data; Missingness; OCT; Ophthalmology; Optic disc; Optical coherence tomography; Optics of the eye; Orientation scores; Population studies; Power analysis; Prediction; Prognosis; Public trust; RegionFinder; Retina; Retinal biomarkers; Retinal datasets; Retinal diseases; Retinal image analysis; Retinal image enhancement; Retinal image preprocessing; Retinal image processing; Retinal imaging; Retinal photography; Retinal vascular changes; Retinal vessel caliber; Retinal vessel datasets; Safe Havens; Sample size; Scanning laser ophthalmoscope; Segmentation; Semiautomated; Sight impairment; Significance; Statistics; Stroke; Testing; Tortuosity; Trusted research environments; Validation; Vascular tree separation; Vessel segmentation; Vessel tracing; Vessel width
