PSM

Exploring Unsupervised Cell Recognition with Prior Self-activation Maps， MICCAI 2023

See our paper here

MoNuSeg dataset

Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology.

BCData

Huang, Z., Ding, Y., Song, G., Wang, L., Chen, J.: BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting.

Arrange your MoNuSeg dataset:

data
-training
---Annotations
----1.png
----2.png
.......
---Tissue Images
----1.png
----2.png
.......

-Testing
----1.png
----2.png
.......

Self-supervised Training

We provide several self-supervised learning methods.

python main_monuseg.py --mode 'train_base'    #Similarity Measure
python main_monuseg.py --mode 'train_contrastive'    #Basic Contrastive learning
python main_monuseg.py --mode 'train_random_rotate'    #Predicting rotate
python main_monuseg.py --mode 'train_simsiam'    #Simsiam
python main_monuseg.py --mode 'train_mean_value'    #Predicting average pixel

Checkpoint of our trained self-supervised network is provided:

Checkpoint trained by train_base

Generate Pseudo Masks

Following the self-supervised training, we can then obtain the pseudo masks using the pre-trained model.

python main_monuseg.py --mode 'generate_label' --model 'model_path'

demo of the PSM and its pseudo label after clustering

Generate Voronoi Labels

python main_monuseg.py --mode 'train_second_stage'  # train a network to get the point prediction
python main_monuseg.py --mode 'generate_voronoi' --model 'model_path' $ get voronoi labels

Train Segmentation Network

python main_monuseg.py --mode 'train_final_stage' 

Test

python main_monuseg.py --mode 'test' --model 'model_path'

Citation

@InProceedings{psm,
author="Chen, Pingyi
and Zhu, Chenglu
and Shui, Zhongyi
and Cai, Jiatong
and Zheng, Sunyi
and Zhang, Shichuan
and Yang, Lin",
title="Exploring Unsupervised Cell Recognition with Prior Self-activation Maps",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="559--568"
}