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2016 | OriginalPaper | Buchkapitel

Deep Retinal Image Understanding

verfasst von : Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez, Luc Van Gool

Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Verlag: Springer International Publishing

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Abstract

This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus images. DRIU uses a base network architecture on which two set of specialized layers are trained to solve both the retinal vessel and optic disc segmentation. We present experimental validation, both qualitative and quantitative, in four public datasets for these tasks. In all of them, DRIU presents super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.

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Vorheriges Kapitel DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

Nächstes Kapitel 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

All the resources of this paper, including code and pre-trained models to reproduce the results, are available at: http://www.vision.ee.ethz.ch/~cvlsegmentation/.

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Titel: Deep Retinal Image Understanding
verfasst von: Kevis-Kokitsi Maninis
Jordi Pont-Tuset
Pablo Arbeláez
Luc Van Gool
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Print ISBN: 978-3-319-46722-1

Electronic ISBN: 978-3-319-46723-8

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46723-8_17

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