Ophthalmic medical image analysis 6th International Workshop, OMIA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, Proceedings / Huazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng (eds.).

Author/creator OMIA (Workshop)
Format Electronic
Publication InfoCham, Switzerland : Springer, [2019]
Descriptionxi, 192 pages : illustrations (black and white) ; 24 cm.
Supplemental ContentFull text available from Springer Nature - Springer Computer Science eBooks 2019 English International
Supplemental ContentFull text available from Springer Books
Subjects
Eye Diseases--diagnostic imaging.
Image Processing, Computer-Assisted
Eye--diagnostic imaging.

Other author/creatorFu, Huazhu.
Other author/creatorGarvin, Mona K.
Other author/creatorMacGillivray, Tom.
Other author/creatorXu, Yanwu.
Other author/creatorZheng, Yalin.
Other author/creatorInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2019 : Shenzhen Shi, China) (22nd :
Variant title OMIA 2019
SeriesLecture notes in computer science, 0302-9743 ; 11855
LNCS sublibrary: SL6 - Image processing, computer vision, pattern recognition, and graphics
Lecture notes in computer science ; 11855. 0302-9743
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
Contents Dictionary Learning Informed Deep Neural Network with Application to OCT Images -- Structure-aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography Image -- Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography -- An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans -- Foveal avascular zone segmentation in clinical routine fluorescein angiographies using multitask learning -- Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries -- 3D-CNN for Glaucoma Detection using Optical Coherence Tomography -- Semi-supervised Adversarial Learning for Diabetic Retinopathy Screening -- Shape Decomposition of Foveal Pit Morphology using Scan Geometry Corrected OCT -- U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography -- Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images -- Robust Optic Disc Localization by Large Scale Learning -- The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detections -- Fundus Image based Retinal Vessel Segmentation Utilizing A Fast and Accurate Fully Convolutional Network -- Network pruning for OCT image classification -- An improved MPB-CNN segmentation method for edema area and neurosensory retinal detachment in SD-OCT images -- Encoder-Decoder Attention Network for Lesion Segmentation of Diabetic Retinopathy -- Multi-Discriminator Generative Adversarial Networks for improved thin retinal vessel segmentation -- Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior -- Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network -- Automated Stage Analysis of Retinopathy of Prematurity Using Joint Segmentation and Multi-Instance Learning -- Retinopathy Diagnosis using Semi-supervised Multi-channel Generative Adversarial Network.
Abstract This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 22 full papers (out of 36 submissions) presented at OMIA 2019 were carefully reviewed and selected. The papers cover various topics in the field of ophthalmic image analysis.
Bibliography noteIncludes bibliographical references and author index.
Access restrictionAvailable only to authorized users.
Other formsAlso issued online.
Technical detailsMode of access: World Wide Web
Source of descriptionOnline resource; title from PDF title page (SpringerLink, viewed October 16, 2019).
Genre/formElectronic books.
ISBN9783030329556 (paperback)
ISBN3030329550 (paperback)
ISBN(ebook)

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