Matlab and python implementation of retinal blood vessel segmentation
·Pytorch
·OpenCV
·PIL
·imageio
·albumentations
·segmentation_models_pytorch
·PyQT
[1] Achintha Iroshan (2020). Segmentation of blood vessels in retinal fundus images using maximum principal curvature (https://www.mathworks.com/matlabcentral/fileexchange/64884-segmentation-of-blood-vessels-in-retinal-fundus-images-using-maximum-principal-curvature), MATLAB Central File Exchange. Retrieved March 19, 2020.
[2] https://github.com/adarshmisra98/Blood-vessel-detection
[3] https://github.com/RobertDachsel/Real-Time_Retinal_Vessel_Segmentation
[4] Dachsel R., Jöster A., Breuß M. (2019) Real-Time Retinal Vessel Segmentation on High-Resolution Fundus Images Using Laplacian Pyramids. In: Lee C., Su Z., Sugimoto A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science, vol 11854. Springer, Cham
[5] https://github.com/Teeerry/retinal-vessels-segment
[6] https://github.com/qubvel/segmentation_models.pytorch
[7] https://github.com/albumentations-team/albumentations
[8] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2015, pp. 234–241