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Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images.

Xiaoxiao Liu1,2, Lei Bi3, Yupeng Xu1,2

  • 1Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.

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Deep learning accurately segments choroidal vessels in swept-source optical coherence tomography (SS-OCT) images. This automated method surpasses human agreement, offering objective analysis for eye diseases.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Accurate choroidal vessel segmentation is crucial for understanding choroid-related diseases.
  • Swept-source optical coherence tomography (SS-OCT) provides high-resolution images of ocular structures.
  • Deep learning methods have shown promise in medical image segmentation.

Purpose of the Study:

  • To evaluate the RefineNet deep learning model for segmenting choroidal vessels in SS-OCT images.
  • To compare the model's performance against manual annotations and inter-observer variability.
  • To establish a new automated approach for quantitative analysis of choroidal vasculature.

Main Methods:

  • The RefineNet deep learning architecture was employed for segmentation.
  • The model was trained and evaluated on 40 SS-OCT images with approximately 3,900 annotated choroidal vessel regions.
  • Segmentation Agreement (SA) was used as the primary evaluation metric.

Main Results:

  • RefineNet achieved a segmentation agreement (SA) of 0.840 ± 0.035 with Clinician 1 and 0.823 ± 0.027 with Clinician 2.
  • The model's performance exceeded the inter-observer variability (SA of 0.821 ± 0.037 between clinicians).
  • Automated segmentation demonstrated high accuracy and reproducibility.

Conclusions:

  • Deep learning, specifically RefineNet, enables accurate and automated segmentation of choroidal vessels from SS-OCT images.
  • This approach provides an objective and reproducible method for quantitative analysis, aiding in the study of choroidal diseases.
  • The findings suggest a significant advancement in analyzing ocular vasculature for clinical and research purposes.