Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images
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Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus imagesData
2020-04Cita bibliográfica
Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images. Computer Methods and Programs in Biomedicine, 186(105201), 105201. doi:10.1016/j.cmpb.2019.105201
É version de
https://doi.org/10.1016/j.cmpb.2019.105201
Resumo
[Abstract]: Background and objectives:The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings and bifurcations is an important requirement for the automated extraction of relevant biomarkers. In that regard, we propose a novel approach that addresses the simultaneous detection of vessel crossings and bifurcations in eye fundus images.
Method: We propose to formulate the detection of vessel crossings and bifurcations in eye fundus images as a multi-instance heatmap regression. In particular, a deep neural network is trained in the prediction of multi-instance heatmaps that model the likelihood of a pixel being a landmark location. This novel approach allows to make predictions using full images and integrates into a single step the detection and distinction of the vascular landmarks.
Results: The proposed method is validated on two public datasets of reference that include detailed annotations for vessel crossings and bifurcations in eye fundus images. The conducted experiments evidence that the proposed method offers a satisfactory performance. In particular, the proposed method achieves 74.23% and 70.90% F-score for the detection of crossings and bifurcations, respectively, in color fundus images. Furthermore, the proposed method outperforms previous works by a significant margin.
Conclusions: The proposed multi-instance heatmap regression allows to successfully exploit the potential of modern deep learning algorithms for the simultaneous detection of retinal vessel crossings and bifurcations. Consequently, this results in a significant improvement over previous methods, which will further facilitate the automated analysis of the retinal vasculature in many pathological conditions.
Palabras chave
Deep learning
Eye fundus
Blood vessels
Crossings
Bifurcations
Landmark detection
Eye fundus
Blood vessels
Crossings
Bifurcations
Landmark detection
Descrición
©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). “Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 186(105201), 105201. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2019.105201.
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Dereitos
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0)
ISSN
0169-2607