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An Alternative and Validated Injection Method for Accessing the Subretinal Space via a Transcleral Posterior Approach
Published on: December 7, 2016
Automated Segmentation of Subretinal Fluid from OCT: A Vision Transformer Approach with Cross-Validation
Julie Midroni1,2, Jack Longwell2, Nishaant Bhambra3
1Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Purpose:
We present an algorithm to segment subretinal fluid (SRF) on individual B-scan slices in patients with rhegmatogenous retinal detachment (RRD). Particular attention is paid to robustness, with a fivefold cross-validation approach and a hold-out test set.
Design:
Retrospective, cross-sectional study.
Participants:
A total of 3819 B-scan slices across 98 time points from 45 patients were used in this study.
Methods:
Subretinal fluid was segmented on all scans. A base SegFormer model, pretrained on 4 massive data sets, was further trained on raw B-scans from the retinal OCT fluid challenge data set of 4532 slices: an open data set of intraretinal fluid, SRF, and pigment epithelium detachment. When adequate performance was reached, transfer learning was used to train the model on our in-house data set, to segment SRF by generating a pixel-wise mask of presence/absence of SRF. A fivefold cross-validation approach was used, with an additional hold-out test set. All folds were first trained and cross-validated and then additionally tested on the hold-out set. Mean (averaged across images) and total (summed across all pixels, irrespective of image) Dice coefficients were calculated for each fold.
Main Outcome Measures:
Subretinal fluid volume after surgical intervention for RRD.
Results:
The average total Dice coefficient across the validation folds was 0.92, the average mean Dice coefficient was 0.82, and the median Dice was 0.92. For the test set, the average total Dice coefficient was 0.94, the average mean Dice coefficient was 0.82, and the median Dice was 0.92. The model showed strong interfold consistency on the hold-out set, with a standard deviation of only 0.03.
Conclusions:
The SegFormer model for SRF segmentation demonstrates a strong ability to segment SRF. This result holds up to cross-validation and hold-out testing, across all folds. The model is available open-source online.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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