HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
Histological Image Segmentation
This repo contains the code to Test and Train the HistoSeg
HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy.
Datasets used for trainig HistoSeg
MoNuSeg – Multi-organ nuclei segmentation from H&E stained histopathological images
link: https://monuseg.grand-challenge.org/
GlaS – Gland segmentation in histology images
link: https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/
Trained Weights are available in the repo to test the HistoSeg
For MoNuSeg Dataset link: https://github.com/saadwazir/HistoSeg/blob/main/HistoSeg_MoNuSeg_.h5
For GlaS Dataset link: https://github.com/saadwazir/HistoSeg/blob/main/HistoSeg_GlaS_.h5
Data Preprocessing for Training
After downloading the dataset you must generate patches of images and their corresponding masks (Ground Truth), & convert it into numpy arrays or you can use dataloaders directly inside the code. you can generate patches using Image_Patchyfy. Link : https://github.com/saadwazir/Image_Patchyfy
For example to train HistoSeg on MoNuSeg Dataset, the distribution of dataset after creating pathes
X_train 1470x256x256x3
y_train 1470x256x256x1
X_val 686x256x256x3
y_Val 686x256x256x1
Data Preprocessing for Testing
You just need to resize the images and their corresponding masks (Ground Truth) into same size i.e all the samples must have same resolution, and then convert it into numpy arrays.
For example to test HistoSeg on MoNuSeg Dataset, the shapes of dataset after creating numpy arrays are
X_test 14x1000x1000x3
y_test 14x1000x1000x1
Requirements
pip install matplotlib
pip install seaborn
pip install tqdm
pip install scikit-learn
conda install tensorflow==2.7
pip install keras==2.2.4
Training
To train HistoSeg use the following command
python HistoSeg_Train.py --train_images 'path' --train_masks 'path' --val_images 'path' --val_masks 'path' --width 256 --height 256 --epochs 100 --batch 16
Testing
To test HistoSeg use the following command
python HistoSeg_Test.py --images 'path' --masks 'path' --weights 'path' --width 1000 --height 1000
For example to test HistoSeg on MoNuSeg Dataset with trained weights, use the following command
python HistoSeg_Test.py --images 'X_test_MoNuSeg_14x1000x1000.npy' --masks 'y_test_MoNuSeg_14x1000x1000.npy' --weights 'HistoSeg_MoNuSeg_.h5' --width 1000 --height 1000
