samaonline/mg-psp

A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images

This repository implements A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images. More specifically, the gland segmentation of meibography images.

Usage

To follow the training routine in train.py you need a DataLoader that yields the tuples of the following format:

(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where

x - batch of input images,

y - batch of groung truth seg maps,

y_cls - batch of 1D tensors of dimensionality N: N total number of classes,

y_cls[i, T] = 1 if class T is present in image i, 0 otherwise

License and Citation

The use of this software is released under CC BY-NC-SA 4.0.

@article{wang2019deep,
  title={A deep learning approach for meibomian gland atrophy evaluation in meibography images},
  author={Wang, Jiayun and Yeh, Thao N and Chakraborty, Rudrasis and Stella, X Yu and Lin, Meng C},
  journal={Translational vision science \& technology},
  volume={8},
  number={6},
  pages={37--37},
  year={2019},
  publisher={The Association for Research in Vision and Ophthalmology}
}