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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Melo, T (Author) Other; Ângela Carneiro (Author) FMUP; Aurélio Campilho (Author) FEUP; Ana Maria Mendonça (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF MEDICAL IMAGING

Vol. 10

ISSN: 2329-4302

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00Y-2N1

DOI: 10.1117/1.jmi.10.1.014006

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19