Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images
CoRR(2024)
Abstract
The choroid is a key vascular layer of the eye, supplying oxygen to the
retinal photoreceptors. Non-invasive enhanced depth imaging optical coherence
tomography (EDI-OCT) has recently improved access and visualisation of the
choroid, making it an exciting frontier for discovering novel vascular
biomarkers in ophthalmology and wider systemic health. However, current methods
to measure the choroid often require use of multiple, independent
semi-automatic and deep learning-based algorithms which are not made
open-source. Previously, Choroidalyzer – an open-source, fully automatic deep
learning method trained on 5,600 OCT B-scans from 385 eyes – was developed to
fully segment and quantify the choroid in EDI-OCT images, thus addressing these
issues. Using the same dataset, we propose a Robust, Resolution-agnostic and
Efficient Attention-based network for CHoroid segmentation (REACH). REACHNet
leverages multi-resolution training with domain-specific data augmentation to
promote generalisation, and uses a lightweight architecture with
resolution-agnostic self-attention which is not only faster than
Choroidalyzer's previous network (4 images/s vs. 2.75 images/s on a standard
laptop CPU), but has greater performance for segmenting the choroid region,
vessels and fovea (Dice coefficient for region 0.9769 vs. 0.9749, vessels
0.8612 vs. 0.8192 and fovea 0.8243 vs. 0.3783) due to its improved
hyperparameter configuration and model training pipeline. REACHNet can be used
with Choroidalyzer as a drop-in replacement for the original model and will be
made available upon publication.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined