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RetOCTNet, a deep learning tool, accurately segments retinal layers in rat optical coherence tomography scans after injury. This automated method aids in monitoring retinal nerve fiber layer thickness and retinal thickness in research.

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Area of Science:

  • Ophthalmology
  • Neuroscience
  • Biomedical Engineering

Background:

  • Retinal ganglion cell (RGC) injury models are crucial for studying optic neuropathies.
  • Accurate segmentation of retinal layers, particularly the retinal nerve fiber layer (RNFL) and total retinal thickness, is essential for quantifying RGC loss.
  • Manual segmentation of optical coherence tomography (OCT) scans is time-consuming and prone to inter-observer variability.

Purpose of the Study:

  • To develop and validate RetOCTNet, a deep learning tool for automated segmentation of RNFL and total retinal thickness from rat OCT scans.
  • To assess the performance of RetOCTNet in various RGC injury models, including ocular hypertension (OHT) and optic nerve crush (ONC).
  • To evaluate the generalizability of RetOCTNet on longitudinal volumetric OCT scans.

Main Methods:

  • Retinal ganglion cell injury was induced via OHT or ONC in rats.
  • Manual segmentation of RNFL and total retinal thickness from radial OCT scans served as ground truth.
  • RetOCTNet was trained and validated using 80% and 10% of the manual segmentations, respectively.
  • The tool's generalizability was tested on volumetric scans from a separate cohort at baseline and up to 12 weeks post-ONC.

Main Results:

  • RetOCTNet achieved high F1 scores: 0.88 for RNFL and 0.98 for retinal thickness in control eyes.
  • Segmentations in OHT and ONC eyes also showed high accuracy (F1 scores: 0.84/0.98 for OHT, 0.78/0.96 for ONC).
  • Longitudinal analysis on volumetric scans revealed significant RNFL and retinal thinning post-ONC.

Conclusions:

  • RetOCTNet accurately segments RNFL and total retinal thickness in both radial and volumetric rat OCT scans.
  • The tool demonstrates robustness across different injury models and scan types.
  • RetOCTNet offers a reliable and efficient method for longitudinal monitoring of RGC injury in rodent models.