Semantic Segmentation for Improved Cell Nuclei Analysis

Tested with python 3.10, Ubuntu 22.04.2 LTS

Installation

1. Some form of conda is required for this project.
   • I recommend Miniconda
   • The larger Anaconda is also an option
2. Create a conda environment.

conda create --name cnsbench python=3.10 -y
conda activate cnsbench

3. Install Pytorch according to the instructions.

# As of 12 Jul 2023
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y

4. Install MMSegmentation with the following instructions.

pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"
pip install "mmsegmentation>=1.0.0"

5. Install left-over dependencies (OpenSlide, ultralytics, scikit-image)

sudo apt -y install openslide-tools #debian/ubuntu
conda install scikit-image -c conda-forge -y
pip install -r requirements.txt

Usage

Each script handles a step of the process, beginning with get_datasets.py

Command line arguments meaning

• dataset: The name of the dataset to train on
• normalised: Whether stain normalised version should be used
• batch: The batch size
• lr: The learning rate
• epochs: The number of epochs for training
• iterations: The number of iterations (total batches) for training
• cache: Allows for disabling caching
• model: Choose either unet or deeplabv3plus
• dataset-root: The base directory for downloaded datasets
• *-interval: the number of iterations before something happens
• wandb: Allows training results to be logged to Weights and Biases

Datasets

To download all datasets, simply run

python get_datasets.py

To change the root directory for downloaded datasets, use the --dataset-root flag

python get_datasets.py --dataset-root ~/my_datasets 

Training

There are training scripts for both YOLOv8 and the MMSegmentation models. To run the YOLOv8 training script on MoNuSeg:

python train_yolo.py --dataset MoNuSeg

To run the YOLOv8 training script on Normalised MoNuSeg images:

python train_yolo.py --dataset MoNuSeg --normalised

To change some hyperparameters (note that --cache DISABLES caching):

python train_yolo.py --dataset MoNuSeg --batch 16 --lr 0.001 --epochs 200 --cache

To run the MMSegmentation training script:

python train.py --dataset MoNuSeg --model deeplabv3plus --normalised

Some other settings

python train.py --dataset MoNuSAC --model unet --wandb --iterations 25000 --val-interval 10000 --log-interval 10 --checkpoint-interval 5000

Visualising

For YOLOv8:

python visualise_yolo.py --dataset MoNuSeg --model <your_model>.pt

Additional options

python visualise_yolo.py --dataset MoNuSeg --model <your_model>.pt --max-det 1000 --normalised --binary --outline

For MMSegmentation

python visualise.py --dataset CryoNuSeg --config <your_config>.py --checkpoint <your ckpt>.pth --binary --outline --normalised

Exporting predictions

For YOLOv8:

python export_yolo.py --model <yolo_model>.pt --dataset TNBC --normalised

For MMSegmentation:

python export.py --checkpoint <your_ckpt>.pth --dataset TNBC --normalised

Evaluation

python evaluate.py --dataset MoNuSeg --compare-root <path_to_model_predictions>

A full example for TNBC (stain normalised)

python train.py --dataset TNBC --model unet --normalised
python export.py --checkpoint "work_dirs/TNBC/unet/<date>/iter_20000*.pth" --dataset TNBC --normalised
python evaluate.py --dataset TNBC --compare-root work_dirs/export/stainnorm/TNBC/unet

To see High Resolution Images from Dicta 2023 Conference Paper Semantic Segmentation for Improved Cell Nuclei Analysis

Semantic Segmentation for Improved Cell Nuclei Analysis