Image Analysis and Machine Learning Platform for Innovation in Diabetic Retinopathy Screening
ARSN is implementing a mass screening for diabetic retinopathy (DR), with the goal of making eye exam of about 75% of identified diabetics. The vision of the consortium SCREEN-DR is to create a distributed and automatic screening platform for DR, based on advanced PACS* management, Machine Learning and Image Analysis, enabling immediate response from health carers, allowing accurate follow-up strategies, and fostering technological innovation. As main objectives we have the automatic image quality assessment, the automatic detection and grading of diabetic retinopathy including the mild non-proliferative, moderate/severe non-proliferative and proliferative grades.
Main Task Goals:
Image Quality Assessment
To automatically reject low quality images that are not suitable for further analysis.
Detection of Normal Images
To automatically discriminate DR fundus images (regardless of DR type and stage) from ‘Normal’ ones.
To grade fundus images into three levels of severity: mild, moderate/severe and proliferative. These levels are characterized by the presence of several lesions, as microaneurisms, exudates, neovascularization, vessel tortuosity, hemorrhages, and venous beading.
Image Web Service
To provide a remote access to all the image analysis functionalities, namely for image quality evaluation, detection of image normality and DR grading.
Vessel Width Estimation demo at: https://goo.gl/VrGVxi
Convolutional Bag of Words demo at: https://goo.gl/odV4QD
Professors: Aurélio Campilho, Ana Maria Mendonça, Jorge Silva
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Articles in Journals:
Pedro Costa, Aurélio Campilho, Convolutional Bag of Words for Diabetic Retinopathy Detection from Eye Fundus Images, IPSJ Transactions on Computer Vision and Applications, 1-6, 2017. DOI 10.1186/s41074-017-0023-6 (Open Access), March 2017. URL: https://goo.gl/hf5Ptj
Maria Inês Meyer, Pedro Costa, Adrian Galdran, Ana Maria Mendonça, and Aurélio Campilho, A deep neural network for vessel tree segmentation of Scanning Laser Ophthalmoscopy Images, ICIAR 2017 – International Conference on Image Analysis and Recognition LNCS 10317, July 2017. URL: https://goo.gl/u4puWX
Pedro Costa, Adrian Galdran, Maria Inês Meyer, Ana Maria Mendonça, and Aurélio Campilho, Adversarial Synthesis of Retinal Images from Vessel Trees, ICIAR 2017 – International Conference on Image Analysis and Recognition LNCS 10317, July 2017. URL: https://goo.gl/ZG2hKS
Beatriz Remeseiro, Ana Maria Mendonça and Aurélio Campilho, Objective Quality Assessment of Retinal Images Based on Texture Analysis, Int. Joint Conference on Neural networks (IJCNN2017), May 2017. URL: https://goo.gl/JmZLBW
Pedro Costa, Aurélio Campilho, Convolutional Bag of Words for Diabetic Retinopathy Detection from Eye Fundus Images, MVA 2017, May 2017. URL: https://goo.gl/3FeRYY
Teresa Araújo, Ana Maria Mendonça and Aurélio Campilho, Ensembles of bagged regression trees for estimation of vessel caliber in retinal images, SPIE Medical Imaging, Orlando, USA, pages 101341K-101341K-8, February 2017. URL: https://goo.gl/qXc21Z
Ana Maria Mendonça, Beatriz Remeseiro, Behdad Dashtbozorg, and Aurélio Campilho, Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs, SPIE Medical Imaging, Orlando, USA, pages 101341L-101341L-7, February 2017. URL: https://goo.gl/7qf2G3
Teresa Araújo, Ana Maria Mendonça, Aurélio Campilho, Vessel width estimation in eye fundus images, pp 87-88, RECPAD 2016 – 22nd Edition of the Portuguese Conference on Pattern Recognition, 2016 (abstract). October 2016. URL: https://goo.gl/4ju0Gx
Pedro Costa, Aurélio Campilho, Convolutional Bag of Words for Diabetic Retinopathy Detection, Bioimaging 2016, pp 32, 2016 (abstract). October 2016. URL: https://goo.gl/itxg79
Aurélio Campilho, Fakhri Karray, Image Analysis and Recognition, Springer Lecture Notes in Computer Science, LNCS 9730, 2016. (820 pages)
Fakhri Karray, Aurélio Campilho, Farida Cheriet, Image Analysis and Recognition, Springer Lecture Notes in Computer Science, LNCS 10317, 2017. (in press)
Pedro Costa, Adrian Galdran, Maria Inês Meyer, Michael David Abràmoff, Meindert Niemeijer, Ana Maria Mendonça, Aurélio Campilho, Towards Adversarial Retinal Image Synthesis, arXiv:1701.08974v1 [cs.CV], January 2017. URL: https://goo.gl/jjvrSG
Researchers: Adrian Galdran, Pedro Costa, Teresa Araújo