Weakly Supervised Nuclei Segmentation using Partial Points Annotation

Description

This repository contains the Pytorch code for the paper:

Weakly Supervised Deep Nuclei Segmentation using Partial Points Annotation in Histopathology Images, TMI2020. (PDF)

If you find this code helpful, please cite our work:

@artcle{Qu2020weakly,
    author = "Qu, Hui and Wu, Pengxiang and Huang, Qiaoying and Yi, Jingru and Yan, Zhennan and Li, Kang 
            and Riedlinger, Gregory and De, Subhajyoti and Zhang, Shaoting and Metaxas, Dimitris",
    title = "Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images",
    journal = "IEEE transactions on Medical Imaging",
    year = "2020",
}

Introduction

Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixelwise annotation for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, self-training with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results reveal that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.

Dependencies

Ubuntu 16.04

Pytorch 1.0.0

Gcc >= 4.9

Usage

Build CRFLoss

GCC version >= 4.9 is required to build the CRF loss correctly.

cd ./code_seg/crf_loss
python setup.py install

Train and test

Put the original images in the folder ./data/MO/images and instance labels in the folder ./data/MO/labels_instance
Specify the image names of train, val, test sets in the json file under ./data/MO
Set the training parameters in code_detection/options.py and code_seg/options.py
Run the script run.sh for detection and segmentation