ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Domain generalization for retinal vessel segmentation via Hessian-based vector field.Medical image analysis · 2024Article
- Segmentation of Low-Light Optical Coherence Tomography Angiography Images under the Constraints of Vascular Network Topology.Sensors (Basel, Switzerland) · 2024Article
- Optical Coherence Tomography Is a Promising Tool for Zebrafish-Based Research-A Review.Bioengineering (Basel, Switzerland) · 2022Review
- Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data.IEEE transactions on medical imaging · 2022Article
- Real-time OCT image denoising using a self-fusion neural network.Biomedical optics express · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and 0.8594 ± 0.0275 on zebrafish data, a dramatic improvement over existing unsupervised algorithms.
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Registered trials
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