DeepDOF: Deep learning extended depth-of-field microscope for fast and slide-free histology

Lingbo Jin¹, Yubo Tang¹, Yicheng Wu, Jackson B. Coole, Melody T. Tan, Xuan Zhao, Hawraa Badaoui, Jacob T. Robinson, Michelle D. Williams, Ann M. Gillenwater, Rebecca R. Richards-Kortum, and Ashok Veeraraghavan

¹ equal contribution

Reference github repository for the paper Deep learning extended depth-of-field microscope. Proceedings of the National Academy of Sciences 117.52 (2020) If you use our dataset or code, please cite our paper:

@article{jin2020deep,
  title={Deep learning extended depth-of-field microscope for fast and slide-free histology},
  author={Jin, Lingbo and Tang, Yubo and Wu, Yicheng and Coole, Jackson B and Tan, Melody T and Zhao, Xuan and Badaoui, Hawraa and Robinson, Jacob T and Williams, Michelle D and Gillenwater, Ann M and others},
  journal={Proceedings of the National Academy of Sciences},
  volume={117},
  number={52},
  pages={33051--33060},
  year={2020},
  publisher={National Acad Sciences}
}

Dataset

Dataset can be downloaded here: the training, validation, and testing dataset used in the manuscript

The dataset contains:

• 600 microscopic fluorescence images of proflavine-stained oral cancer resections (10×/0.25-NA, manual refocusing)
• 600 histopathology images of healthy and cancerous tissue of human brain, lungs, mouth, colon, cervix, and breast from The Cancer Genome Atlas (TCGA) Cancer FFPE slides.
• 600 INRIA Holiday dataset

In total, it contains 1,800 images (each 1,000 × 1,000 pixels; gray scale)

The 1,800 images were randomly assigned to training, validation, and testing sets that contained 1,500; 150; and 150 images, respectively

Code

dependencies

Required packages and versions can be found in deepDOF.yml. It can also be used to create a conda environment.

training

We use a 2 step training process. Step 1 (DeepDOF_step1.py) does not update the optical layer and only trains the U-net. Step 2 (DeepDOF_step2.py) jointly optimizes both the optical layer and the U-net

testing

To test the trained network with an image, use test_image_all_720um.py

Reference

Wu, Yicheng, et al. "Phasecam3d—learning phase masks for passive single view depth estimation." 2019 IEEE International Conference on Computational Photography (ICCP). IEEE, 2019.