LSUnetMix

We proposed a LSUnetMix modle which contains 3 modules: CIT (Channel Information Transmission), MDC (Multiscale Dilated Convolution), and PE (Prospect Enhancement).

Requirements

Install from the requirement.txt using:

pip install -r requirements.txt

Usage

1. Data Preparation

1.1. GlaS and MoNuSeg Datasets

The original data can be downloaded in following links:

• MoNuSeG Dataset - Link (Original)
• GLAS Dataset - Link (Original)
• DRIVE Dataset - Link (Original)

Then prepare the datasets in the following format for easy use of the code:

├── datasets
    ├── DRIVE
    │   ├── Val_Folder
    │   │   ├── img
    │   │   └── labelcol
    │   └── Train_Folder
    │       ├── img
    │       └── labelcol
    ├── GlaS
    │   ├── Val_Folder
    │   │   ├── img
    │   │   └── labelcol
    │   └── Train_Folder
    │       ├── img
    │       └── labelcol
    └── MoNuSeg
        ├── Val_Folder
        │   ├── img
        │   └── labelcol
        └── Train_Folder
            ├── img
            └── labelcol

2. Training

First, change the settings in Config.py, all the configurations including learning rate, batch size and etc. are in it.

Run:

python train_model.py

3. Testing

3.1. Test the Model and Visualize the Segmentation Results

First, change the model_path and set the relative dataset in Config.py. Then run:

python test_model.py

You can get the Dice and IoU scores and the visualization results.

3.2. The pre-trained models of us.

We provide the best models in in our paper, and here is the links.

• MoNuSeG model - Link (Original)
• GLAS model - Link (Original)
• GDRIVE model - Link (Original)