TY - CHAP
T1 - Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation
AU - Perdomo, Oscar
AU - Andrearczyk, Vincent
AU - Meriaudeau, Fabrice
AU - Müller, Henning
AU - González, Fabio A.
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - Glaucoma is an ophthalmic disease related to damage in the optic nerve and it is without symptoms in its early stages. Left untreated, it can lead to vision limitation and blindness. Eye fundus images have been widely accepted by medical personnel to examine the morphology and texture of the optic nerve head and the physiologic cup but glaucoma diagnosis is still subjective and without clear consensus among experts. This paper presents a multi-stage deep learning model for glaucoma diagnosis based on a curriculum learning strategy. In curriculum learning, a model is sequentially trained to solve incrementally difficult tasks. Our proposed model includes the following stages: segmentation of the optic disc and physiological cup, prediction of morphometric features from segmentations, and prediction of disease level (healthy, suspicious and glaucoma). The experimental evaluation shows that our proposed method outperforms conventional convolutional deep learning models from the state of the art reported on the RIM-ONE-v1 and DRISHTI-GS1 datasets with an accuracy of 89.4% and an AUC of 0.82 respectively.
AB - Glaucoma is an ophthalmic disease related to damage in the optic nerve and it is without symptoms in its early stages. Left untreated, it can lead to vision limitation and blindness. Eye fundus images have been widely accepted by medical personnel to examine the morphology and texture of the optic nerve head and the physiologic cup but glaucoma diagnosis is still subjective and without clear consensus among experts. This paper presents a multi-stage deep learning model for glaucoma diagnosis based on a curriculum learning strategy. In curriculum learning, a model is sequentially trained to solve incrementally difficult tasks. Our proposed model includes the following stages: segmentation of the optic disc and physiological cup, prediction of morphometric features from segmentations, and prediction of disease level (healthy, suspicious and glaucoma). The experimental evaluation shows that our proposed method outperforms conventional convolutional deep learning models from the state of the art reported on the RIM-ONE-v1 and DRISHTI-GS1 datasets with an accuracy of 89.4% and an AUC of 0.82 respectively.
UR - http://www.scopus.com/inward/record.url?scp=85053901480&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-00949-6_38
DO - 10.1007/978-3-030-00949-6_38
M3 - Chapter
AN - SCOPUS:85053901480
SN - 9783030009489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 319
EP - 327
BT - Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Taylor, Zeike
A2 - Bogunovic, Hrvoje
A2 - Snead, David
A2 - Garvin, Mona K.
A2 - Chen, Xin Jan
A2 - Ciompi, Francesco
A2 - Xu, Yanwu
A2 - Maier-Hein, Lena
A2 - Veta, Mitko
A2 - Trucco, Emanuele
A2 - Stoyanov, Danail
A2 - Rajpoot, Nasir
A2 - van der Laak, Jeroen
A2 - Martel, Anne
A2 - McKenna, Stephen
PB - Springer
T2 - 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -