IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

Model Checkpoints to reproduce the results for the journal paper "IterMiUnet: A lightweight architecture for automatic blood vessel segmentation" arxiv journal

• Since this is an old work standalone keras 2.2.4 was used while training the models as opposed tf.keras which comes integrated with tensorflow now.
• The jupyter notebook inside Stare folder can be run using the older version of standalone keras
• We have shared the model checkpoints for the Stare dataset in Stare/Models, where 20 models were trained using leave-one-out validation. For example, Stare/Models/Stareiternetim0001.keras was trained using all 19 images except im0001 and will be tested on im0001 during prediction, and similarly for the other 19 models.

Citation

@article{kumar2023itermiunet,
  title={IterMiUnet: A lightweight architecture for automatic blood vessel segmentation},
  author={Kumar, Ashish and Agrawal, RK and Joseph, Leve},
  journal={Multimedia Tools and Applications},
  volume={82},
  number={28},
  pages={43207--43231},
  year={2023},
  publisher={Springer}
}