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Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets.

Sunil Kumar Yadav1,2, Rahele Kafieh1, Hanna Gwendolyn Zimmermann1

  • 1Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13125 Berlin, Germany.

Journal of Imaging
|May 27, 2022
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Summary
This summary is machine-generated.

We developed a new AI method to accurately measure retinal layer thickness from OCT scans, crucial for diagnosing neurodegenerative diseases like Multiple Sclerosis. This tool improves upon existing methods, offering more reliable biomarker quantification for research and clinical use.

Keywords:
U-Netdeep learningintraretinal layer segmentationoptical coherence tomography (OCT)retina

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Area of Science:

  • Ophthalmology and Neuroscience
  • Artificial Intelligence in Medical Imaging
  • Biomarker Discovery

Background:

  • Neurodegenerative diseases like Multiple Sclerosis (MS), Alzheimer's, and Parkinson's lack reliable biomarkers for neurodegeneration and neuroinflammation.
  • Intraretinal layer thickness measured via Optical Coherence Tomography (OCT) offers a promising, noninvasive method to assess neuroretinal structures.
  • Current OCT segmentation methods struggle with subtle changes and weak gradients, hindering reliable research and clinical application.

Purpose of the Study:

  • To develop and validate a robust, automated method for segmenting intraretinal layers from OCT B-scans.
  • To address the unmet need for accurate and reproducible neuroretinal biomarkers in central nervous system disorders.
  • To improve the efficiency and reliability of OCT-based neurodegeneration assessment.

Main Methods:

  • Proposed a cascaded two-stage U-Net based network (CCU-INSEG) for retinal tissue and intraretinal layer segmentation.
  • Implemented post-processing steps including Laplacian-based outlier detection and adaptive non-linear interpolation for hole filling.
  • Utilized a weighted focal loss function to handle foreground-background pixel imbalance during training.
  • Trained the model on 17,458 B-scans from Multiple Sclerosis patients and healthy controls.

Main Results:

  • Achieved a mean absolute error (MAE) of 2.3 μm compared to manual segmentation on MS patient data, outperforming state-of-the-art methods.
  • Demonstrated strong performance on external glaucoma datasets with MAE of 2.6 μm and 3.7 μm.
  • Significantly reduced segmentation rejection rates in severe optic atrophy cases (3.5% vs. 41.4% for a graph-based method).

Conclusions:

  • The proposed CCU-INSEG method provides robust and accurate segmentation of intraretinal layers from OCT scans.
  • This automated approach shows potential for reliable quantification of neuroretinal biomarkers in various central nervous system disorders.
  • The method's high fidelity and robustness, even in cases of severe neuroretinal changes, support its clinical and research applicability.