An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net

By Qing Xu, Wenting Duan

MICCAI COMPAY 2021 paper: An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net

Architecture

Requirements

1. pytorch >=1.5.0
2. pytorch-lightning==1.1.0
3. albumentations

Dataset

A public microscopy image dataset from 2018 Data Science Bowl grand challenge:

https://www.kaggle.com/c/data-science-bowl-2018/data/

Train

You first need to download the public dataset or prepare your private dataset (with 2018 Data Science Bowl format). An example of training the model is:

python train.py --dataset train_set --loss combined --batch 8 --lr 0.001 --epoch 200

Evaluation

python eval.py --dataset test_set --model checkpoints/model_1.pth

Visualisation

python predict.py --dataset test_set --model checkpoints/model_1.pth

Citation

@InProceedings{pmlr-v156-xu21a,
  title = 	 {An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net},
  author =       {Xu, Qing and Duan, Wenting},
  booktitle = 	 {Proceedings of the MICCAI Workshop on Computational Pathology}, 
  pages = 	 {236--245},
  year = 	 {2021}
}