Ophthalmic Medical Image Analysis: 6th International Workshop, OMIA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings

202 Pages·2019·40.156 MB·English

by  Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng

Huazhu Fu · Mona K. Garvin ·
Tom MacGillivray · Yanwu Xu ·
Yalin Zheng (Eds.)
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Image Analysis
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6th International Workshop, OMIA 2019
Held in Conjunction with MICCAI 2019
Shenzhen, China, October 17, Proceedings
Lecture Notes in Computer Science 11855
Founding Editors
Gerhard Goos
Karlsruhe Institute of Technology, Karlsruhe, Germany
Juris Hartmanis
Cornell University, Ithaca, NY, USA
Editorial Board Members
Elisa Bertino
Purdue University, West Lafayette, IN, USA
Wen Gao
Peking University, Beijing, China
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Gerhard Woeginger
RWTH Aachen, Aachen, Germany
Moti Yung
Columbia University, New York, NY, USA
More information about this series at http://www.springer.com/series/7412
Huazhu Fu Mona K. Garvin
(cid:129) (cid:129)
Tom MacGillivray Yanwu Xu
(cid:129) (cid:129)
Yalin Zheng (Eds.)
Ophthalmic Medical
Image Analysis
6th International Workshop, OMIA 2019
Held in Conjunction with MICCAI 2019
Shenzhen, China, October 17
Proceedings
123
Editors
Huazhu Fu MonaK.Garvin
Inception Institute University of Iowa
of Artificial Intelligence Iowa City,IA, USA
AbuDhabi, UnitedArab Emirates
YanwuXu
TomMacGillivray Baidu, Inc.
University of Edinburgh Beijing,China
Edinburgh,UK
YalinZheng
TheUniversity of Liverpool
Liverpool, UK
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-030-32955-6 ISBN978-3-030-32956-3 (eBook)
https://doi.org/10.1007/978-3-030-32956-3
LNCSSublibrary:SL6–ImageProcessing,ComputerVision,PatternRecognition,andGraphics
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Preface
The6thInternationalWorkshoponOphthalmicMedicalImageAnalysis(OMIA2019)
was held in Shenzhen, China, on October 17, 2019, in conjunction with the 22nd
International Conference on Medical Image Computing and Computer-Assisted
Intervention (MICCAI).
Age-relatedmaculardegeneration,diabeticretinopathy,and glaucoma arethe main
causesofblindnessinbothdevelopedanddevelopingcountries.Thecostofblindness
to society and individuals is huge, and many cases can be avoided by early
intervention. Early and reliable diagnosis strategies and effective treatments are
therefore a world priority. At the same time, there is mounting research on the retinal
vasculature and neuro-retinal architecture as a source of biomarkers for several
high-prevalence conditions like dementia, cardiovascular disease, and of course
complications of diabetes. Automatic and semi-automatic software tools for retinal
image analysis are being used widely in retinal biomarkers research, and increasingly
percolating into clinical practice. Significant challenges remain in terms of reliability
and validation, number and type of conditions considered, multi-modal analysis (e.g.,
fundus,opticalcoherencetomography,scanninglaserophthalmoscopy),novelimaging
technologies, and the effective transfer of advanced computer vision and machine
learningtechnologies,tomentionafew.Theworkshopaddressedalltheseaspectsand
more, in the ideal interdisciplinary context of MICCAI.
This workshop aimed to bring together scientists, clinicians, and students from
multiple disciplines in the growing ophthalmic image analysis community, such as
electronic engineering, computer science, mathematics, and medicine, to discuss the
latest advancementsinthefield.Atotal of36full-length papers weresubmitted tothe
workshop in response to the call for papers. All submissions are double-blind
peer-reviewed by at least three members of the Program Committee. Paper selection
was based on methodological innovation, technical merit, results, validation, and
applicationpotential.Finally,22paperswereacceptedfororals(8papers)andposters
(14papers)attheworkshopandchosentobeincludedinthisSpringerLNCSvolume.
We are grateful to the Program Committee for reviewing the submitted papers and
giving constructive comments andcritiques, tothe authors for submitting high-quality
papers, to the presenters for excellent presentations, and to all the OMIA 2019
attendees for coming to Shenzhen from all around the world.
September 2019 Huazhu Fu
Mona K. Garvin
Tom MacGillivray
Yanwu Xu
Yalin Zheng
Organization
Workshop Organizers
Huazhu Fu Inception Institute of Artificial Intelligence, UAE
Mona K. Garvin University of Iowa, USA
Tom MacGillivray University of Edinburgh, UK
Yanwu Xu Baidu Inc., China
Yalin Zheng University of Liverpool, UK
Program Committee
Bhavna Antony IBM Research, Australia
Guozhen Chen Shenzhen University, China
Min Chen University of Pennsylvania, USA
Qiang Chen Nanjing University of Science and Technology, China
Jun Cheng Cixi Institute of Biomedical Engineering, Chinese
Academy of Sciences, China
Dongxu Gao University of Liverpool, UK
Mohammad Hamghalam Shenzhen University, China
Baiying Lei Shenzhen university, China
Xiaomeng Li The Chinese University of Hong Kong, SAR China
Dwarikanath Mahapatra IBM Research, Australia
Emma Pead University of Edinburgh, UK
Suman Sedai IBM Research, Australia
Fei Shi Soochow University, China
Raphael Sznitman University of Bern, Switzerland
Mingkui Tan South China University of Technology, China
Ruwan Tennakoon RMIT University, Australia
Jui-Kai Wang University of Iowa, USA
Dehui Xiang Soochow University, China
Mengdi Xu Yitu Technology, Singapore
Yuguang Yan South China University of Technology, China
Jiong Zhang University of Southern California, USA
Yitian Zhao Cixi Institute of Biomedical Engineering, Chinese
Academy of Sciences, China
Yuanjie Zheng Shandong Normal University, China
Weifang Zhu Soochow University, China
Contents
Dictionary Learning Informed Deep Neural Network
with Application to OCT Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Joshua Bridge, Simon P. Harding, Yitian Zhao, and Yalin Zheng
Structure-Aware Noise Reduction Generative Adversarial Network
for Optical Coherence Tomography Image. . . . . . . . . . . . . . . . . . . . . . . . . 9
YanGuo,KangWang,SuhuiYang,YueWang,PengGao,GuotongXie,
Chuanfeng Lv, and Bin Lv
Region-Based Segmentation of Capillary Density in Optical Coherence
Tomography Angiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Wenxiang Deng, Michelle R. Tamplin, Isabella M. Grumbach,
Randy H. Kardon, and Mona K. Garvin
An Amplified-Target Loss Approach for Photoreceptor Layer
Segmentation in Pathological OCT Scans. . . . . . . . . . . . . . . . . . . . . . . . . . 26
José Ignacio Orlando, Anna Breger, Hrvoje Bogunović, Sophie Riedl,
Bianca S. Gerendas, Martin Ehler, and Ursula Schmidt-Erfurth
Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein
Angiographies Using Multitask Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Dominik Hofer, José Ignacio Orlando, Philipp Seeböck,
Georgios Mylonas, Felix Goldbach, Amir Sadeghipour,
Bianca S. Gerendas, and Ursula Schmidt-Erfurth
Guided M-Net for High-Resolution Biomedical Image Segmentation
with Weak Boundaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Shihao Zhang, Yuguang Yan, Pengshuai Yin, Zhen Qiu, Wei Zhao,
Guiping Cao, Wan Chen, Jin Yuan, Risa Higashita, Qingyao Wu,
Mingkui Tan, and Jiang Liu
3D-CNN for Glaucoma Detection Using Optical Coherence Tomography. . . . 52
Yasmeen George, Bhavna Antony, Hiroshi Ishikawa, Gadi Wollstein,
Joel Schuman, and Rahil Garnavi
Semi-supervised Adversarial Learning for Diabetic
Retinopathy Screening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Sijie Liu, Jingmin Xin, Jiayi Wu, and Peiwen Shi
Shape Decomposition of Foveal Pit Morphology Using Scan
Geometry Corrected OCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
MinChen,JamesC.Gee,JessicaI.W.Morgan,andGeoffreyK.Aguirre
x Contents
U-Net with Spatial Pyramid Pooling for Drusen Segmentation
in Optical Coherence Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Rhona Asgari, Sebastian Waldstein, Ferdinand Schlanitz,
Magdalena Baratsits, Ursula Schmidt-Erfurth, and Hrvoje Bogunović
Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell
Segmentation in Adaptive Optics Retinal Images. . . . . . . . . . . . . . . . . . . . . 86
Jianfei Liu, Christine Shen, Tao Liu, Nancy Aguilera, and Johnny Tam
Robust Optic Disc Localization by Large Scale Learning. . . . . . . . . . . . . . . 95
Shilu Jiang, Zhiyuan Chen, Annan Li, and Yunhong Wang
The Channel Attention Based Context Encoder Network for Inner
Limiting Membrane Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Hao Qiu, Zaiwang Gu, Lei Mou, Xiaoqian Mao, Liyang Fang,
Yitian Zhao, Jiang Liu, and Jun Cheng
Fundus Image Based Retinal Vessel Segmentation Utilizing
a Fast and Accurate Fully Convolutional Network. . . . . . . . . . . . . . . . . . . . 112
Junyan Lyu, Pujin Cheng, and Xiaoying Tang
Network Pruning for OCT Image Classification . . . . . . . . . . . . . . . . . . . . . 121
Bing Yang, Yi Zhang, Jun Cheng, Jin Zhang, Lei Mou, Huaying Hao,
Yitian Zhao, and Jiang Liu
An Improved MPB-CNN Segmentation Method for Edema Area
and Neurosensory Retinal Detachment in SD-OCT Images. . . . . . . . . . . . . . 130
Jian Fang, Yuhan Zhang, Keren Xie, Songtao Yuan, and Qiang Chen
Encoder-Decoder Attention Network for Lesion Segmentation
of Diabetic Retinopathy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Shuanglang Feng, Weifang Zhu, Heming Zhao, Fei Shi, Zuoyong Li,
and Xinjian Chen
Multi-discriminator Generative Adversarial Networks for Improved
Thin Retinal Vessel Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
Gabriel Tjio, Shaohua Li, Xinxing Xu, Daniel Shu Wei Ting, Yong Liu,
and Rick Siow Mong Goh
Fovea Localization in Fundus Photographs by Faster R-CNN
with Physiological Prior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Jun Wu, Jiapei Wang, Jie Xu, Yiting Wang, Kaiwei Wang,
Zongjiang Shang, Dayong Ding, Xirong Li, Gang Yang, Xuemin Jin,
Yanting Wang, Fangfang Dai, and Jianping Fan
Contents xi
Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis
via a Deep Convolutional Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Rugang Zhang, Jinfeng Zhao, Guozhen Chen, Tianfu Wang,
Guoming Zhang, and Baiying Lei
Automated Stage Analysis of Retinopathy of Prematurity Using Joint
Segmentation and Multi-instance Learning. . . . . . . . . . . . . . . . . . . . . . . . . 173
Guozhen Chen, Jinfeng Zhao, Rugang Zhang, Tianfu Wang,
Guoming Zhang, and Baiying Lei
Retinopathy Diagnosis Using Semi-supervised Multi-channel
Generative Adversarial Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
Yingpeng Xie, Qiwei Wan, Guozhen Chen, Yanwu Xu, and Baiying Lei
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191