Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography
Retinal layer segmentation in Optical Coherence Tomography (OCT) is a fundamental step for extracting quantitative biomarkers of retinal structure. Indeed, there is a growing interest in the analysis of OCTs in the context of neurodegenerative diseases. However, segmentation remains challenging due to speckle noise, shadowing artifacts, low contrast between adjacent layers, anatomical variability across subjects, and domain shifts arising from different acquisition protocols and clinical populations. While deep learning methods have achieved remarkable performance, their robustness and general
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- LinkedLinked via arxiv author · 85%Iker Moran-Cavero →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- LinkedLinked via arxiv author · 85%Monica Hernandez →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- LinkedLinked via arxiv author · 85%Elvira Mayordomo →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- LinkedLinked via arxiv author · 85%Naiara Artiaga →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- LinkedLinked via arxiv author · 85%Beatriz Pardiñas →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- LinkedLinked via arxiv author · 85%Beatriz Cordon →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- LinkedLinked via arxiv author · 85%Elena Garcia-Martin →
“Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography”
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: martin”