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Retinal vessel segmentation and depiction of morphological attributes of retinal blood vessels, such as length, width, tortuosity, branching patterns and angles are utilized for the diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension, arteriosclerosis. Automatic det…
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  • This Project is created by the joint efforts of Rohit Sharma , Abdul Mugeesh and Kanishk Nama . Kanishk Nama is responsible for preparing and loading the dataset, Rohit Sharma is responsible for developing the model while Abdul Mugeesh is responsible for calculating and loading the results. Rest of the work is done together.

DRIVE - Digital Retinal Images for Vessel Extraction

We have done segmentation of blood vessels from their respective retinal images. As this is a segmentation problem, we have used U-net architecture for the segmentation purpose. This is a binary classification task as at each pixel, we have to classify whether that pixel is feature or not.


We have used DRIVE dataset for training and testing purpose, this dataset consists of 40 images, 20 for training purpose and 20 for testing.


  • We are using DenseBlock U-net, the architecture is same as U-net but only difference is we are replacing 1 convolutional layer with Dense layer.


base : store template class of data_loader trainer infer model

model : store the definition of semantic segmention model

infer : how to predict

trainer : the way to train your model

Get Started

  1. First Clone the github repository.
  • For Training Purposes First, you have to set all the paths in the files wherever necessary, in config directory and then run
  • For Testing Purpose You have to run the file, All the results will be stored in experiments/VesselNet/checkpoint folder.
  1. To modify the model, you can first change the path in all the python files and then change or to add the dense layer change thr file.

Our Results

ROC curve

Models Comparsion

Our Research

We have written a Research Paper based on our work and as a future part we are aspiring to add more Dense layers and performing hyper-parameter tuning.


  • On Team Level, We are thankful to Sir Vipul Kumar Mishra for teaching and brushing important Deep learning concepts during the workshop, thank you sir. Also, we would like to thank our Mentor Dr. Suneet Kumar Gupta and Bennett University to give us this prestigious opportunity.
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