Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework

Tânia Melo; Ângela Carneiro; Aurélio Campilho; Ana Maria Mendonça

doi:10.1117/1.JMI.10.1.014006

21 February 2023 Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework

Tânia Melo, Ângela Carneiro, Aurélio Campilho, Ana Maria Mendonça

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 1, 014006 (February 2023). https://doi.org/10.1117/1.JMI.10.1.014006

Abstract

Purpose

The development of accurate methods for retinal layer and fluid segmentation in optical coherence tomography images can help the ophthalmologists in the diagnosis and follow-up of retinal diseases. Recent works based on joint segmentation presented good results for the segmentation of most retinal layers, but the fluid segmentation results are still not satisfactory. We report a hierarchical framework that starts by distinguishing the retinal zone from the background, then separates the fluid-filled regions from the rest, and finally, discriminates the several retinal layers.

Approach

Three fully convolutional networks were trained sequentially. The weighting scheme used for computing the loss function during training is derived from the outputs of the networks trained previously. To reinforce the relative position between retinal layers, the mutex Dice loss (included for optimizing the last network) was further modified so that errors between more “distant” layers are more penalized. The method’s performance was evaluated using a public dataset.

Results

The proposed hierarchical approach outperforms previous works in the segmentation of the inner segment ellipsoid layer and fluid (Dice coefficient = 0.95 and 0.82, respectively). The results achieved for the remaining layers are at a state-of-the-art level.

Conclusions

The proposed framework led to significant improvements in fluid segmentation, without compromising the results in the retinal layers. Thus, its output can be used by ophthalmologists as a second opinion or as input for automatic extraction of relevant quantitative biomarkers.

Citation Download Citation

Tânia Melo, Ângela Carneiro, Aurélio Campilho, and Ana Maria Mendonça "Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework," Journal of Medical Imaging 10(1), 014006 (21 February 2023). https://doi.org/10.1117/1.JMI.10.1.014006

Received: 5 August 2022; Accepted: 27 January 2023; Published: 21 February 2023

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KEYWORDS

Image segmentation

Education and training

Optical coherence tomography

Retina

Matrices

Deep learning

Lithium

Purpose

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