2 research outputs found
Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study
Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to
blindness and cardiovascular disease. Information about early stage T2D might
be present in retinal fundus images, but to what extent these images can be
used for a screening setting is still unknown. In this study, deep neural
networks were employed to differentiate between fundus images from individuals
with and without T2D. We investigated three methods to achieve high
classification performance, measured by the area under the receiver operating
curve (ROC-AUC). A multi-target learning approach to simultaneously output
retinal biomarkers as well as T2D works best (AUC = 0.746 [0.001]).
Furthermore, the classification performance can be improved when images with
high prediction uncertainty are referred to a specialist. We also show that the
combination of images of the left and right eye per individual can further
improve the classification performance (AUC = 0.758 [0.003]), using a
simple averaging approach. The results are promising, suggesting the
feasibility of screening for T2D from retinal fundus images.Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6
pages, 1 figur
Approximation of a pipeline of unsupervised retina image analysis methods with a CNN
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