Multi-task-deep-network

Multi-task deep learning based approaches for semantic segmentation in medical images

Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation (EMBC 2019) Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation (MICCAIW - MLMI 2019)

Dependencies

Packages

PyTorch
TensorboardX
OpenCV
numpy
tqdm

An exhaustive list of packages used could be found in the requirements.txt file. Install the same using the following command:

 conda create --name <env> --file requirements.txt

Preprocessing

Contour and Distance Maps are pre-computed and can be obtained from binary mask. Sample matlab codes can be found here:

Contour: https://in.mathworks.com/help/images/ref/bwperim.html
Distance: https://in.mathworks.com/help/images/ref/bwdist.html

Directory Structure

Train and Test folders should contain the following structure:

├── contour
    |-- 1.png
    |-- 2.png
    ...
├── dist_contour
    |--1.mat 
    |--2.mat
    ...
├── dist_mask
    |-- 1.mat
    |-- 2.mat
    ...
├── dist_signed
    |-- 1.mat
    |-- 2.mat
    ...
├── image
    |-- 1.jpg
    |-- 2.jpg
    ...
└── mask
    |-- 1.png
    |-- 2.png
    ...

Train code

UNET

base_path=''
train_path=${base_path}'/train/image' 
val_path=${base_path}'/test/image' 
model_type='unet'
object_type='polyp'
save_path=${base_path}'/models'
python train.py --train_path ${train_path} 
                --val_path ${val_path} 
                --model_type ${model_type} 
                --object_type ${object_type} 
                --save_path ${save_path}

DCAN

base_path=''
train_path=${base_path}'/train/image' 
val_path=${base_path}'/test/image' 
model_type='convmcd'
object_type='dcan'
save_path=${base_path}'/models'
python train.py --train_path ${train_path} 
                --val_path ${val_path} 
                --model_type ${model_type} 
                --object_type ${object_type} 
                --save_path ${save_path}

DMTN

base_path=''
train_path=${base_path}'/train/image' 
val_path=${base_path}'/test/image' 
model_type='dmtn'
object_type='polyp'
save_path=${base_path}'/models'
python train.py --train_path ${train_path} 
                --val_path ${val_path} 
                --model_type ${model_type} 
                --object_type ${object_type} 
                --save_path ${save_path}

Psi-Net

base_path=''
train_path=${base_path}'/train/image' 
val_path=${base_path}'/test/image' 
model_type='psinet'
object_type='polyp'
save_path=${base_path}'/models'
python train.py --train_path ${train_path} 
                --val_path ${val_path} 
                --model_type ${model_type} 
                --object_type ${object_type} 
                --save_path ${save_path}

Conv-MCD

base_path=''
train_path=${base_path}'/train/image' 
val_path=${base_path}'/test/image' 
model_type='convmcd'
object_type='polyp'
save_path=${base_path}'/models'
python train.py --train_path ${train_path} 
                --val_path ${val_path} 
                --model_type ${model_type} 
                --object_type ${object_type} 
                --save_path ${save_path}

Citations

If you use the Conv-MCD or Psi-Net code in your research, please consider citing the respective paper:

@article{Murugesan2019PsiNetSA,
  title={Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation},
  author={Balamurali Murugesan and Kaushik Sarveswaran and Sharath M. Shankaranarayana and Keerthi Ram and Mohanasankar Sivaprakasam},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.04099}
}

@misc{murugesan2019convmcd,
    title={Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation},
    author={Balamurali Murugesan and Kaushik Sarveswaran and Sharath M Shankaranarayana and Keerthi Ram and Jayaraj Joseph and Mohanasankar Sivaprakasam},
    year={2019},
    eprint={1908.05311},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Bala93 / Multi-task-deep-network