Skip to content
BCDU-Net : Medical Image Segmentation
Python
Branch: master
Clone or download
Latest commit 23a16ba Nov 2, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Lung Segmentation
Retina Blood Vessel Segmentation
Skin Lesion Segmentation Update train_isic18.py Sep 26, 2019
output_images
Bi_Directional_ConvLSTM_U_Net_with_Densely_Connected_Convolutions.pdf Add files via upload Aug 29, 2019
README.md

README.md

Bi-Directional ConvLSTM U-Net with Densely Connected Convolutions

Deep auto-encoder-decoder network for medical image segmentation with state of the art results on skin lesion segmentation, lung segmentation, and retinal blood vessel segmentation. This method applies bidirectional convolutional LSTM layers in U-net structure to non-linearly encode both semantic and high-resolution information with non-linearly technique. Furthermore, it applies densely connected convolution layers to include collective knowledge in representation and boost convergence rate with batch normalization layers. If this code helps with your research please consider citing the following paper:

R. Azad, M. Asadi, Mahmood Fathy and Sergio Escalera "Bi-Directional ConvLSTM U-Net with Densely Connected Convolutions ", ICCV, 2019, download link.

Please consider starring us, if you found it useful. Thanks

Updates

Prerequisties and Run

This code has been implemented in python language using Keras libarary with tensorflow backend and tested in ubuntu OS, though should be compatible with related environment. following Environement and Library needed to run the code:

  • Python 3
  • Keras - tensorflow backend

Run Demo

For training deep model for each task, go to the related folder and follow the bellow steps:

Skin Lesion Segmentation

1- Download the ISIC 2018 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18.
2- Run Prepare_ISIC2018.py for data preperation and dividing data to train,validation and test sets.
3- Run train_isic18.py for training BCDU-Net model using trainng and validation sets. The model will be train for 100 epochs and it will save the best weights for the valiation set. You can also train U-net model for this dataset by changing model to unet, however, the performance will be low comparing to BCDU-Net.
4- For performance calculation and producing segmentation result, run evaluate.py. It will represent performance measures and will saves related figures and results in output folder.

Retina Blood Vessel Segmentation

1- Download Drive dataset from this link and extract both training and test folders in a folder name DRIVE (make a new folder with name DRIVE)
2- Run prepare_datasets_DRIVE.py for reading whole data. This code will read all the train and test samples and will saves them as a hdf5 file in the DRIVE_datasets_training_testing folder.
3- The next step is to extract random patches from the training set to train the model, to do so, Run save_patch.py, it will extract random patches with size 64*64 and will save them as numpy file. This code will use help_functions.py, spre_processing.py and extract_patches.py functions for data normalization and patch extraction.
4- For model training, run train_retina.py, it will load the training data and will use 20% of training samples as a validation set. The model will be train for 50 epochs and it will save the best weights for the valiation set.
4- For performance calculation and producing segmentation result, run evaluate.py. It will represent performance measures and will saves related figures and results in test folder.
Note: For image pre-processing and patch extraction we used this github's code.

Lung Segmentation

1- Download the Lung Segmentation dataset from Kaggle link and extract it.
2- Run Prepare_data.py for data preperation, train/test seperation and generating new masks around the lung tissues. 3- Run train_lung.py for training BCDU-Net model using trainng and validation sets (20 percent of the training set). The model will be train for 50 epochs and it will save the best weights for the valiation set. You can train either BCDU-net model with 1 or 3 densly connected convolutions.
4- For performance calculation and producing segmentation result, run evaluate_performance.py. It will represent performance measures and will saves related figures and results.

Quick Overview

Diagram of the proposed method

Structure of the Bidirection Convolutional LSTM that used in our network

Diagram of the proposed method

Results

For evaluating the performance of the proposed method, Two challenging task in medical image segmentaion has been considered. In bellow, results of the proposed approach illustrated.

Task 1: Retinal Blood Vessel Segmentation

Performance Comparision on Retina Blood Vessel Segmentation

In order to compare the proposed method with state of the art appraoches on retinal blood vessel segmentation, we considered Drive dataset.

Methods Year F1-scores Sensivity Specificaty Accuracy AUC
Chen etc. all Hybrid Features 2014 - 0.7252 0.9798 0.9474 0.9648
Azzopardi et. all Trainable COSFIRE filters 2015 - 0.7655 0.9704 0.9442 0.9614
Roychowdhury and et. all Three Stage Filtering 2016 - 0.7250 0.9830 0.9520 0.9620
Liskowsk etc. allDeep Model 2016 - 0.7763 0.9768 0.9495 0.9720
Qiaoliang et. all Cross-Modality Learning Approach 2016 - 0.7569 0.9816 0.9527 0.9738
Ronneberger and et. all U-net 2015 0.8142 0.7537 0.9820 0.9531 0.9755
Alom etc. all Recurrent Residual U-net 2018 0.8149 0.7726 0.9820 0.9553 0.9779
Oktay et. all Attention U-net 2018 0.8155 0.7751 0.9816 0.9556 0.9782
Alom et. all R2U-Net 2018 0.8171 0.7792 0.9813 0.9556 0.9784
Azad et. all Proposed BCDU-Net 2019 0.8222 0.8012 0.9784 0.9559 0.9788

Retinal blood vessel segmentation result on test data

Retinal Blood Vessel Segmentation result 1 Retinal Blood Vessel Segmentation result 2 Retinal Blood Vessel Segmentation result 3

Skin Lesion Segmentation

Performance Evalution on the Skin Lesion Segmentation task

Methods Year F1-scores Sensivity Specificaty Accuracy PC JS
Ronneberger and etc. all U-net 2015 0.647 0.708 0.964 0.890 0.779 0.549
Alom et. all Recurrent Residual U-net 2018 0.679 0.792 0.928 0.880 0.741 0.581
Oktay et. all Attention U-net 2018 0.665 0.717 0.967 0.897 0.787 0.566
Alom et. all R2U-Net 2018 0.691 0.726 0.971 0.904 0.822 0.592
Azad et. all Proposed BCDU-Net 2019 0.847 0.783 0.980 0.936 0.922 0.936

Skin Lesion Segmentation results

Skin Lesion Segmentation result 1 Skin Lesion Segmentation result 1 Skin Lesion Segmentation result 1 Skin Lesion Segmentation result 1

Lung Segmentation

Performance Evalution on the Lung Segmentation task

Methods Year F1-scores Sensivity Specificaty Accuracy AUC JS
Ronneberger and etc. all U-net 2015 0.9658 0.9696 0.9872 0.9872 0.9784 0.9858
Alom et. all Recurrent Residual U-net 2018 0.9638 0.9734 0.9866 0.9836 0.9800 0.9836
Alom et. all R2U-Net 2018 0.9832 0.9944 0.9832 0.9918 0.9889 0.9918
Azad et. all Proposed BCDU-Net 2019 0.9904 0.9910 0.9982 0.9972 0.9946 0.9972

Lung Segmentation results

Lung Segmentation result 1 Lung Segmentation result 2 Lung Segmentation result 3

Model weights

You can download the learned weights for each task in the following table.

Task Dataset Learned weights
Retina Blood Vessel Segmentation Drive BCDU_net_D3
Skin Lesion Segmentation ISIC2018 BCDU_net_D3
Lung Segmentation Lung kaggle BCDU_net_D3

Query

All implementation done by Reza Azad. For any query please contact us for more information.

rezazad68@gmail.com
You can’t perform that action at this time.