Our layer segmentation algorithm is applied to Optical Coherence Tomography (OCT) b-scans acquired with the Heidelberg Spectralis.
It performs well on normal scans but may not give reliable results in the case of pathology.
Deep learning model implemented in PyTorch trained on data of Heidelberg scans and validated in an independent, external, real-life dataset demonstrating human-level performance. The model runs on AWS lambda. It takes as input a macular OCT volume and produces segmentation maps.
A PDF report is returned which contains B-scans visualisation of the segmentation output.
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