maberyick/nucleiSegmentationHEDL

Nuclei segmentation performed on the digitized H&E-stained images of whole slide images (WSI). It is a deep learning model (DL) based on a GAN architecture.

Nuclei Segmentation Model (Python-DL)

Nuclei segmentation performed on the digitized H&E-stained images of whole slide images (WSI). It is a deep learning model (DL) based on a GAN architecture.

Pre-requsites

• Linux (Tested on windows10, CUDA10.2)
• NVIDIA GPU (Tested on Nvidia TianX 1080 x 12 on local workstations)
• Python (3.5+), matplotlib (3.1.1), numpy (1.17.3), opencv-python (4.1.1.26), pillow (6.2.1), PyTorch (1.5+), scikit-learn (0.22.1), scipy (1.3.1), torchvision (0.4.2), tensorboardx (optional)

All directly running to automatically install all depending libraries.

pip install -r requirement.txt 

How to run

Model checkpoint is save in model folder. To run the nuclei pixel prediction, simply run

python main_PredviaTrainGenerator.py

The input image for nuclei pixel prediction should be put into data folder. Different image format handling wrappers were also provided by main_PredviaTrainGenerator_IMAGEEXTENSION.py, change the IMAGEEXTENSION with the provided extension names and place the corresponding images into data folder.