MultitaskOCTA

This repository is an official PyTorch implementation of paper:

"BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images", MICCAI 2021. [Student Travel Award] paper link: BSDA-Net

"Multi-task Learning Based Ocular Disease Discrimination and FAZ Segmentation Utilizing OCTA Images", EMBC 2021. paper link: paper

MICCAI 2021

EMBC 2021 [Coming soon]

Dependencies

Packages

• Python 3.7
• PyTorch >= 1.7.0
• Numpy
• Sklearn
• Segmentation Models Pytorch
• TensorboardX
• OpenCV
• numpy
• Tqdm
• surface-distance

Datasets

Now our processed datasets are avaliable here: AliDrive and GoogleDrive

Data Preprocessing

Using the file pre_dis.m in Matlab formula for image preprocessing to generate Boundary Heatmaps and Signed distanced maps (SDMs) for training BSDA-Net.

Run octaaug.py to start preprocessing automatically with preset directory value to make augment OCTA images, which is stored in local directory.

Directory Structure

├── contour
   └── 1.png
   └── 2.png
   ...
├── dist_contour
   └── 1.mat
   └── 2.mat
   ...
├── dist_mask
   └── 1.mat
   └── 2.mat
   ...
├── dist_signed_01
   └── 1.mat
   └── 2.mat
   ...
├── dist_signed_11 (used in the MICCAI paper)
   └── 1.mat
   └── 2.mat
   ...
├──image
   └── 1.jpg
   └── 2.jpg
   ...
├── mask
   └── 1.jpg
   └── 2.jpg
   ...

Training Code

To start training, you should set the parameters used for training:

• train_path: Training image path.
• val_path: Validation image path.
• test_path: Testing image path.
• save_path: Path for saving results.
• train-type: Training type, including single classification & segmentation, cotraining or multitask.
• model_type: Used for single segmentation, cotraining or multitask. The segmentation architecture used for training.
• batch_size: Batch size for training stage.
• val_batch_size: Batch size for validation.
• num_epochs: Total number of epochs for training stage.
• use_pretrained: Use pretrained weight on ImageNet or not.
• loss_type: Loss used for training stage.
• LR_seg: Learning rate setting for segmentation process.
• LR_clf: Learning rate setting for classification process.
• classnum: Used for single classification, cotraining or multitask. Class number for classification.

For simply start training, you can use our preset shell file named Demo.sh with prepared dataset stored in local path. Or you can set the parameters listed above to define your own training architecture. The results will be stored at the local path with your dataset name as a folder named as model_type+loss_type.

Testing code

To start testing, you should set the parameters that is used for training to load the model file correctly:

• train_path: Training image path.
• val_path: Validation image path.
• test_path: Testing image path.
• save_path: Path for saving results.
• train-type: Training type, including single classification & segmentation, cotraining or multitask.
• model_type: Used for single segmentation, cotraining or multitask. The segmentation architecture used for training.
• val_batch_size: Batch size for validation.
• use_pretrained: Use pretrained weight on ImageNet or not.
• loss_type: Loss used for training stage.

Results

From left to right, they are respectively representation of segmentation results of FAZ using different models. The bottom line represents corresponding boudnary heatmaps and signed distance maps for groundtruth.

Citation

L. Lin, Z. Wang, J. Wu, Y. Huang, J. Lyu, P. Cheng, J. Wu, X. Tang*, "BSDA-Net: a boundary shape and distance aware joint learning framework for segmenting and classifying OCTA images", In the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Strasbourg, France, September 2021.

Contact

Li Lin (linli@eee.hku.hk)

Zhonghua Wang (Wzhjerry1112@gmail.com)

Acknowledgements

Thanks for segmentation models pytorch for the implementation of the segmentation codes.