Awesome
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}
}