QuickAnnotator

Quick Annotator is an open-source digital pathology annotation tool.

Purpose

Machine learning approaches for segmentation of histologic primitives (e.g., cell nuclei) in digital pathology (DP) Whole Slide Images (WSI) require large numbers of exemplars. Unfortunately, annotating each object is laborious and often intractable even in moderately sized cohorts. The purpose of the quick annotator is to rapidly bootstrap annotation creation for digital pathology projects by helping identify images and small regions.

Because the classifier is likely to struggle by intentionally focusing in these areas, less pixel level annotations are needed, which significantly improves the efficiency of the users. Our approach involves updating a u-net model while the user provides annotations, so the model is then in real time used to produce. This allows the user to either accept or modify regions of the prediction.

Requirements

Tested with Python 3.8

Requires:

Python
pip

And the following additional python package:

Flask_SQLAlchemy
scikit_image
scikit_learn
opencv_python_headless
scipy
requests
SQLAlchemy
torch
torchvision 10.Flask_Restless
numpy
Flask
umap_learn
Pillow
tensorboardX
ttach
albumentations
config

You can likely install the python requirements using something like (note python 3+ requirement):

pip3 install -r requirements.txt

The library versions have been pegged to the current validated ones. Later versions are likely to work but may not allow for cross-site/version reproducibility

We received some feedback that users could installed torch. Here, we provide a detailed guide to install Torch

Torch's Installation

The general guides for installing Pytorch can be summarized as following:

Check your NVIDIA GPU Compute Capability @ https://developer.nvidia.com/cuda-gpus
Download CUDA Toolkit @ https://developer.nvidia.com/cuda-downloads
Install PyTorch command can be found @ https://pytorch.org/get-started/locally/

Basic Usage

see UserManual for a demo

Run

 E:\Study\Research\QA\qqqqq\test1\quick_annotator>python QA.py

By default, it will start up on localhost:5555

Warning: virtualenv will not work with paths that have spaces in them, so make sure the entire path to env/ is free of spaces.

Config Sections

There are many modular functions in QA whose behaviors could be adjusted by hyper-parameters. These hyper-parameters can be set in the config.ini file

[common]
[flask]
[cuda]
[sqlalchemy]
[pooling]
[train_ae]
[train_tl]
[make_patches]
[make_embed]
[get_prediction]
[frontend]
[superpixel]

Advanced Usage

See wiki

Citation

PDF file available for download

Please use below to cite this paper if you find this repository useful or if you use the software shared here in your research.

  @misc{miao2021quick,
      title={Quick Annotator: an open-source digital pathology based rapid image annotation tool}, 
      author={Runtian Miao and Robert Toth and Yu Zhou and Anant Madabhushi and Andrew Janowczyk},
      year={2021},
      eprint={2101.02183},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
  }

Frequently Asked Questions

See FAQ

phromo/QuickAnnotator