zs1314/OCTAMamba
【ICASSP2025 Oral】Offical Pytorch Code for "OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation"
This project helps eye care professionals and researchers precisely identify and outline blood vessels in Optical Coherence Tomography Angiography (OCTA) scans. You input an OCTA image of a patient's retina, and it outputs a highly accurate segmentation map highlighting the intricate vascular network. This is for ophthalmologists, optometrists, and medical researchers who analyze retinal images for diagnosing and monitoring eye conditions like diabetic retinopathy or glaucoma.
No commits in the last 6 months.
Use this if you need to automatically and accurately segment retinal vasculature from OCTA images, especially for complex cases with noise or multi-scale vessel structures.
Not ideal if you are looking for a general-purpose medical image segmentation tool beyond OCTA retinal vasculature or if you prefer a non-Linux operating system for your analysis.
Stars
34
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 10, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zs1314/OCTAMamba"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
ProjectNeura/MIPCandy
Build a complete experiment pipeline for your PyTorch MIP model in 10 seconds.
tbepler/topaz
Pipeline for particle picking in cryo-electron microscopy images using convolutional neural...
canlab/CanlabCore
Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is...
MPI-Dortmund/tomotwin-cryoet
cryo-ET particle picking by representation and metric learning
bioimage-io/core-bioimage-io-python
Python libraries for loading, running and packaging bioimage.io models