Building imaging biomarkers from retinal OCT — from annotation protocol to trained model.
I develop quantitative image-analysis pipelines for retinal OCT and other ophthalmic imaging in a multicenter uveitis cohort.
- Deep-learning segmentation
- Annotation methodology
- Observer variability
- Imaging biomarkers
- Reproducible analysis pipelines
| Domain | Focus |
|---|---|
| Medical image analysis | Segmentation, registration, quality control at scale |
| Biomedical data science | Study design, evaluation metrics, statistical reporting |
| Ophthalmic imaging | OCT, retinal layer & lesion quantification |
| Microscopy | Fluorescence image analysis, single-cell segmentation |
| Sequencing | RNA-seq, association analysis (GWAS/TWAS) |
- BioMeds @ CCTB Würzburg — automated segmentation of high-resolution fluorescence microscopy for single-cell Influenza A vRNA replication analysis (Cellpose), in cooperation with the HIRI
- Transcriptome-wide association analysis on Arabidopsis
Languages
Deep learning & image analysis
Data & analysis
Infrastructure
Open to exchange on segmentation benchmarking, annotation quality and clinical imaging pipelines.