Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network

Monserrate Intriago-Pazmiño; Julio Ibarra-Fiallo; Raúl Alonso-Calvo; José Crespo

doi:10.1145/3368691.3368711

Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network

Monserrate Intriago-Pazmiño, Julio Ibarra-Fiallo, Raúl Alonso-Calvo, José Crespo

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Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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Resumen

In this paper, we describe the experimentation with a convolutional neural network for segmenting retinal net from pathological fundus images of preterm born children. Segmenting retinal net from pathological fundus images is a fundamental task to aid computer diagnosis. We used U-net architecture for training and testing. Testing with ROPFI dataset, we obtained an area under the receiver operating curve equal to 0.9180; when average sensitivity is equal to 0.700, the average specificity is equal to 0.9710. This performance is higher than prior works using a similar dataset.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
Editorial	Association for Computing Machinery
ISBN (versión digital)	9781450372848
DOI	https://doi.org/10.1145/3368691.3368711
Estado	Publicada - 2 dic. 2019
Evento	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 - Dubai, Emiratos Árabes Unidos Duración: 2 dic. 2019 → 5 dic. 2019

Serie de la publicación

Nombre	ACM International Conference Proceeding Series

Conferencia

Conferencia	2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019
País/Territorio	Emiratos Árabes Unidos
Ciudad	Dubai
Período	2/12/19 → 5/12/19

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10.1145/3368691.3368711

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Intriago-Pazmiño, M., Ibarra-Fiallo, J., Alonso-Calvo, R., & Crespo, J. (2019). Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 Artículo a20 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3368691.3368711

Intriago-Pazmiño, Monserrate ; Ibarra-Fiallo, Julio ; Alonso-Calvo, Raúl et al. / Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019. Association for Computing Machinery, 2019. (ACM International Conference Proceeding Series).

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title = "Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network",

abstract = "In this paper, we describe the experimentation with a convolutional neural network for segmenting retinal net from pathological fundus images of preterm born children. Segmenting retinal net from pathological fundus images is a fundamental task to aid computer diagnosis. We used U-net architecture for training and testing. Testing with ROPFI dataset, we obtained an area under the receiver operating curve equal to 0.9180; when average sensitivity is equal to 0.700, the average specificity is equal to 0.9710. This performance is higher than prior works using a similar dataset.",

keywords = "Convolutional neural network, Medical image processing, Retinopathy of Prematurity",

author = "Monserrate Intriago-Pazmi{\~n}o and Julio Ibarra-Fiallo and Ra{\'u}l Alonso-Calvo and Jos{\'e} Crespo",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery. ACM; 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019 ; Conference date: 02-12-2019 Through 05-12-2019",

year = "2019",

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doi = "10.1145/3368691.3368711",

language = "Ingl{\'e}s",

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Intriago-Pazmiño, M, Ibarra-Fiallo, J, Alonso-Calvo, R & Crespo, J 2019, Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019., a20, ACM International Conference Proceeding Series, Association for Computing Machinery, 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019, Dubai, Emiratos Árabes Unidos, 2/12/19. https://doi.org/10.1145/3368691.3368711

Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. / Intriago-Pazmiño, Monserrate; Ibarra-Fiallo, Julio; Alonso-Calvo, Raúl et al.
Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019. Association for Computing Machinery, 2019. a20 (ACM International Conference Proceeding Series).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network

AU - Intriago-Pazmiño, Monserrate

AU - Ibarra-Fiallo, Julio

AU - Alonso-Calvo, Raúl

AU - Crespo, José

PY - 2019/12/2

Y1 - 2019/12/2

N2 - In this paper, we describe the experimentation with a convolutional neural network for segmenting retinal net from pathological fundus images of preterm born children. Segmenting retinal net from pathological fundus images is a fundamental task to aid computer diagnosis. We used U-net architecture for training and testing. Testing with ROPFI dataset, we obtained an area under the receiver operating curve equal to 0.9180; when average sensitivity is equal to 0.700, the average specificity is equal to 0.9710. This performance is higher than prior works using a similar dataset.

AB - In this paper, we describe the experimentation with a convolutional neural network for segmenting retinal net from pathological fundus images of preterm born children. Segmenting retinal net from pathological fundus images is a fundamental task to aid computer diagnosis. We used U-net architecture for training and testing. Testing with ROPFI dataset, we obtained an area under the receiver operating curve equal to 0.9180; when average sensitivity is equal to 0.700, the average specificity is equal to 0.9710. This performance is higher than prior works using a similar dataset.

KW - Convolutional neural network

KW - Medical image processing

KW - Retinopathy of Prematurity

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Intriago-Pazmiño M, Ibarra-Fiallo J, Alonso-Calvo R, Crespo J. Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. En Proceedings of the 2nd International Conference on Data Science, E-Learning and Information Systems, DATA 2019. Association for Computing Machinery. 2019. a20. (ACM International Conference Proceeding Series). doi: 10.1145/3368691.3368711

Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network

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