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.

MJ2695 / DeepDOF