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AW-Net: A Novel Fully Connected Attention-based Medical Image Segmentation Model

This repository contains the official codebase of AW-Net which was accepted in ICCV workshop 2023. AW-Net focuses on segmenting multimodal medical images such as MRI, PET, and CT scan images for multiple organs such as brain, breast, and spine.

Overview

A novel fully connected segmentation model which provides a solution to problem of segmenting multi-modal 3D/4D medical images by incorporating a novel regularized transient block. AW-Net uses L1 regularizers followed by dropout layers to improve model performance. The implementation is inspired from Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray Code | Paper.

AW-Net Architecture

Model Architecture

Datasets

The datasets used in the paper can be downloaded from the links below:

Code Implementation

Environment Setup

The code is developed on tensorflow. In order to run the script file follow the instructions below:

  1. Create a new environment using python -m /path/to/env
  2. Fork the repository. Move the AW-Net folder to the new environment folder i.e. /path/to/env
  3. Use command cd /path/to/env/AW-Net in the command prompt to access the files
  4. There is a requirement.txt file. Run pip install -r requirement.txt in the command prompt to install the required python packages.

Network Training

We provide script files to train AW-Net on the BraTS2020 dataset. The dataloader.py works only for images and masks in numpy (.npy) format. The pre-processed version of BraTS2020 dataset can be accesed here link. The main.py contains script file to run the model. The following steps are required to execute the AW-Net architecture:

  • The function expects the path for the directory in which input images are present (arg1)

  • The function expects the path for the directory in which input masks are present (arg2)

  • The function expects the directory where the weights will be saved after the successful training of the model (arg3)

  • The default arguments in the function include num_classes (number of classes present in the mask), val_samples (number of images to be considered as a validation sample during splitting), epochs, and batch_size. Feel free to experiment on these as per the requirement.

  • In order to train the AW-Net model, run

    cd /path/to/env/AW-Net
python main.py arg1 arg2 arg3

    The saved weights will be stored under the path path/to/env/AW-Net/Data as .h5 file

    Network Prediction

    In order to display the prediction generated by the model for a single image, run the following code snipet:

    import numpy as np
import matplotlib.pyplot as plt
from model import*
model=AW_Net((image_height,image_width,image_channel),num_classes, dropout_rate=0.0, batch_norm=True)
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.load_weights('path/to/env/AW-Net/Data/weights.h5')
test_image = np.load('path/to/env/AW-Net/Data/Image/0.npy')
predicted_image=model.predict(test_image.reshape(1,image_height,image_width,image_channel))
plt.imshow(np.argmax(predicted_image,axis=-1))

    Set image_height = 128, image_width = 128, image_channel = 3, num_classes = 4 for BraTS2020 data.

Citation

If this code is used for research, please cite our paper:

MLA

Pal, Debojyoti, et al. "AW-Net: A Novel Fully Connected Attention-Based Medical Image Segmentation Model." Proceedings of the IEEE/CVF International 
Conference on Computer Vision. 2023.

Bibtex

@inproceedings{pal2023aw,
title={AW-Net: A Novel Fully Connected Attention-Based Medical Image Segmentation Model},
author={Pal, Debojyoti and Meena, Tanushree and Mahapatra, Dwarikanath and Roy, Sudipta},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={2532--2541},
year={2023}
}