Approximation of a pipeline of unsupervised retina image analysis methods with a CNN

Friso Heslinga, Josien Pluim, Behdad Dasht Bozorg, Tos Berendschot, Alfons J.H.M. Houben, Ronald M.A. Henry, Mitko Veta

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

A pipeline of unsupervised image analysis methods for extraction of geometrical features from retinal fundus images has previously been developed. Features related to vessel caliber, tortuosity and bifurcations, have been identified as potential biomarkers for a variety of diseases, including diabetes and Alzheimer's. The current computationally expensive pipeline takes 24 minutes to process a single image, which impedes implementation in a screening setting. In this work, we approximate the pipeline with a convolutional neural network (CNN) that enables processing of a single image in a few seconds. As an additional benefit, the trained CNN is sensitive to key structures in the retina and can be used as a pretrained network for related disease classification tasks. Our model is based on the ResNet-50 architecture and outputs four biomarkers that describe global properties of the vascular tree in retinal fundus images. Intraclass correlation coefficients between the predictions of the CNN and the results of the pipeline showed strong agreement (0.86 - 0.91) for three of four biomarkers and moderate agreement (0.42) for one biomarker. Class activation maps were created to illustrate the attention of the network. The maps show qualitatively that the activations of the network overlap with the biomarkers of interest, and that the network is able to distinguish venules from arterioles. Moreover, local high and low tortuous regions are clearly identified, confirming that a CNN is sensitive to key structures in the retina.
LanguageEnglish
Title of host publicationImage Processing
Subtitle of host publicationSPIE Medical Imaging, 2019, San Diego, California, United States
EditorsElsa D. Angelini, Bennett A. Landman
Place of PublicationBellingham
PublisherSPIE
Number of pages7
DOIs
StatePublished - 1 Mar 2019
EventSPIE Medical Imaging 2019 - San Diego, United States
Duration: 16 Feb 201921 Feb 2019
http://spie.org/MI/entireprogram/2019-2-20?print=2&SSO=1

Publication series

NameProceedings of SPIE
Volume10949
ISSN (Electronic)1605-7422

Conference

ConferenceSPIE Medical Imaging 2019
CountryUnited States
CitySan Diego
Period16/02/1921/02/19
Internet address

Fingerprint

Biomarkers
Image analysis
Pipelines
Neural networks
Medical problems
Feature extraction
Screening
Chemical activation
Processing

Cite this

Heslinga, F., Pluim, J., Dasht Bozorg, B., Berendschot, T., Houben, A. J. H. M., Henry, R. M. A., & Veta, M. (2019). Approximation of a pipeline of unsupervised retina image analysis methods with a CNN. In E. D. Angelini, & B. A. Landman (Eds.), Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States [109491N] (Proceedings of SPIE; Vol. 10949). Bellingham: SPIE. DOI: 10.1117/12.2512393
Heslinga, Friso ; Pluim, Josien ; Dasht Bozorg, Behdad ; Berendschot, Tos ; Houben, Alfons J.H.M. ; Henry, Ronald M.A. ; Veta, Mitko. / Approximation of a pipeline of unsupervised retina image analysis methods with a CNN. Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States. editor / Elsa D. Angelini ; Bennett A. Landman. Bellingham : SPIE, 2019. (Proceedings of SPIE).
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abstract = "A pipeline of unsupervised image analysis methods for extraction of geometrical features from retinal fundus images has previously been developed. Features related to vessel caliber, tortuosity and bifurcations, have been identified as potential biomarkers for a variety of diseases, including diabetes and Alzheimer's. The current computationally expensive pipeline takes 24 minutes to process a single image, which impedes implementation in a screening setting. In this work, we approximate the pipeline with a convolutional neural network (CNN) that enables processing of a single image in a few seconds. As an additional benefit, the trained CNN is sensitive to key structures in the retina and can be used as a pretrained network for related disease classification tasks. Our model is based on the ResNet-50 architecture and outputs four biomarkers that describe global properties of the vascular tree in retinal fundus images. Intraclass correlation coefficients between the predictions of the CNN and the results of the pipeline showed strong agreement (0.86 - 0.91) for three of four biomarkers and moderate agreement (0.42) for one biomarker. Class activation maps were created to illustrate the attention of the network. The maps show qualitatively that the activations of the network overlap with the biomarkers of interest, and that the network is able to distinguish venules from arterioles. Moreover, local high and low tortuous regions are clearly identified, confirming that a CNN is sensitive to key structures in the retina.",
author = "Friso Heslinga and Josien Pluim and {Dasht Bozorg}, Behdad and Tos Berendschot and Houben, {Alfons J.H.M.} and Henry, {Ronald M.A.} and Mitko Veta",
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Heslinga, F, Pluim, J, Dasht Bozorg, B, Berendschot, T, Houben, AJHM, Henry, RMA & Veta, M 2019, Approximation of a pipeline of unsupervised retina image analysis methods with a CNN. in ED Angelini & BA Landman (eds), Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States., 109491N, Proceedings of SPIE, vol. 10949, SPIE, Bellingham, SPIE Medical Imaging 2019, San Diego, United States, 16/02/19. DOI: 10.1117/12.2512393

Approximation of a pipeline of unsupervised retina image analysis methods with a CNN. / Heslinga, Friso; Pluim, Josien; Dasht Bozorg, Behdad; Berendschot, Tos; Houben, Alfons J.H.M.; Henry, Ronald M.A.; Veta, Mitko.

Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States. ed. / Elsa D. Angelini; Bennett A. Landman. Bellingham : SPIE, 2019. 109491N (Proceedings of SPIE; Vol. 10949).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - A pipeline of unsupervised image analysis methods for extraction of geometrical features from retinal fundus images has previously been developed. Features related to vessel caliber, tortuosity and bifurcations, have been identified as potential biomarkers for a variety of diseases, including diabetes and Alzheimer's. The current computationally expensive pipeline takes 24 minutes to process a single image, which impedes implementation in a screening setting. In this work, we approximate the pipeline with a convolutional neural network (CNN) that enables processing of a single image in a few seconds. As an additional benefit, the trained CNN is sensitive to key structures in the retina and can be used as a pretrained network for related disease classification tasks. Our model is based on the ResNet-50 architecture and outputs four biomarkers that describe global properties of the vascular tree in retinal fundus images. Intraclass correlation coefficients between the predictions of the CNN and the results of the pipeline showed strong agreement (0.86 - 0.91) for three of four biomarkers and moderate agreement (0.42) for one biomarker. Class activation maps were created to illustrate the attention of the network. The maps show qualitatively that the activations of the network overlap with the biomarkers of interest, and that the network is able to distinguish venules from arterioles. Moreover, local high and low tortuous regions are clearly identified, confirming that a CNN is sensitive to key structures in the retina.

AB - A pipeline of unsupervised image analysis methods for extraction of geometrical features from retinal fundus images has previously been developed. Features related to vessel caliber, tortuosity and bifurcations, have been identified as potential biomarkers for a variety of diseases, including diabetes and Alzheimer's. The current computationally expensive pipeline takes 24 minutes to process a single image, which impedes implementation in a screening setting. In this work, we approximate the pipeline with a convolutional neural network (CNN) that enables processing of a single image in a few seconds. As an additional benefit, the trained CNN is sensitive to key structures in the retina and can be used as a pretrained network for related disease classification tasks. Our model is based on the ResNet-50 architecture and outputs four biomarkers that describe global properties of the vascular tree in retinal fundus images. Intraclass correlation coefficients between the predictions of the CNN and the results of the pipeline showed strong agreement (0.86 - 0.91) for three of four biomarkers and moderate agreement (0.42) for one biomarker. Class activation maps were created to illustrate the attention of the network. The maps show qualitatively that the activations of the network overlap with the biomarkers of interest, and that the network is able to distinguish venules from arterioles. Moreover, local high and low tortuous regions are clearly identified, confirming that a CNN is sensitive to key structures in the retina.

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DO - 10.1117/12.2512393

M3 - Conference contribution

T3 - Proceedings of SPIE

BT - Image Processing

PB - SPIE

CY - Bellingham

ER -

Heslinga F, Pluim J, Dasht Bozorg B, Berendschot T, Houben AJHM, Henry RMA et al. Approximation of a pipeline of unsupervised retina image analysis methods with a CNN. In Angelini ED, Landman BA, editors, Image Processing: SPIE Medical Imaging, 2019, San Diego, California, United States. Bellingham: SPIE. 2019. 109491N. (Proceedings of SPIE). Available from, DOI: 10.1117/12.2512393