Heatmap-guided balanced multi-task learning approach for glistening characterization in OCT images
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http://hdl.handle.net/2183/41015
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Heatmap-guided balanced multi-task learning approach for glistening characterization in OCT imagesAuthor(s)
Date
2025-06Abstract
[Abstract]: Glistening in intraocular lenses (IOLs) refers to the formation of water-filled microvacuoles within the lens material, typically assessed via slit-lamp (SL) photography. Recent proposals, like Optical Coherence Tomography (OCT) combined with deep learning, offer promising alternatives for evaluating glistenings. This study proposes a novel, automated multi-task method for detecting glistenings and segmenting the IOL area (IOLa) using heatmap-guided segmentation. Exhaustive experimentation demonstrates the method's generalization and stability across diverse IOL models with varying glistening severity, offering a dataset 375% more variable than previous studies. The study focuses on two tasks: glistening detection (Task I) and IOLa segmentation (Task II). For Task I, two single-task baselines were tested: a classical deep learning approach (a) and heatmap-guided segmentation (b). The best configurations achieved Intraclass Correlation Coefficient (ICC) scores of 0.901 for baseline (a) and 0.946 for baseline (b), while the multi-task approach reached 0.939. In Task II, improvements in segmentation accuracy were less pronounced but still achieved high Dice scores, consistently above 0.910. This research is the first to apply balanced multi-task strategies and heatmap-guided segmentation for glistening detection and to tackle IOLa segmentation. The method's competitive results confirm the value of deep learning for characterizing glistenings in OCT images, utilizing a more diverse and clinically representative dataset. This approach advances the field by providing a more reliable and efficient method for glistening evaluation.
Keywords
Deep learning
Glistening
Intraocular lens
Multi-task
OCT
Glistening
Intraocular lens
Multi-task
OCT
Description
©2025 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Biomedical Signal Processing and Control. The Version of Record is available online at https://doi.org/10.1016/j.bspc.2025.107527
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1746-8094