| # Structured crowdsourcing enables convolutional segmentation of histology images | |
| This repo contains the necessary information and download instructions to download the dataset associated with the paper: | |
| ***_Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083_*** | |
| This data can be visualized in a public instance of the DSA at https://goo.gl/cNM4EL. | |
| ------------------------------------------------- | |
| ## Usage | |
| - Each mask is a .png image, where pixel values encode region class membership. The meaning of ground truth encoded can be found at the file `./meta/gtruth_codes.tsv`. | |
| - The name of each mask encodes all necessary information to extract the corresponding RGB images from TCGA slides. For convenience, RGBs are also downloaded using the code used here. | |
| - **[CRITICAL] -** Please be aware that zero pixels represent regions outside the region of interest (“don’t care” class) and should be assigned zero-weight during model training; they do **NOT** represent an “other” class. | |
| - The RGBs and corresponding masks will be at the set `MPP` resolution. If `MPP` was set to `None`, then they | |
| would be at `MAG` magnification. If both are set to `None`, then they will be at the base (scan) magnification. | |
| ------------------------------------------------- | |
| ## Download (Single link - convenient) | |
| You can use [this link](https://drive.google.com/drive/folders/1zqbdkQF8i5cEmZOGmbdQm-EP8dRYtvss?usp=sharing) to download the dataset at 0.25 MPP resolution. | |
| ------------------------------------------------- | |
| ## Download (command line - flexible) | |
| Use this to download all elements of the dataset using the command line. | |
| This script will download any or all of the following: | |
| - annotation JSON files (coordinates) | |
| - masks | |
| - RGB images | |
| Steps are as follows: | |
| **Step 0: Clone this repo** | |
| ```bash | |
| $ git clone https://github.com/CancerDataScience/CrowdsourcingDataset-Amgadetal2019 | |
| $ cd CrowdsourcingDataset-Amgadetal2019 | |
| ``` | |
| **Step 1: Instal requirements** | |
| `pip install girder_client girder-client pillow numpy scikit-image imageio` | |
| **Step 2 (optional): Edit `configs.py`** | |
| If you like, you may edit various download configurations. Of note: | |
| - `SAVEPATH` - where everything will be saved | |
| - `MPP` - microns per pixel for RGBs and masks (preferred, default is 0.25) | |
| - `MAG` - magnification (overridden by `MPP` if `MPP` is set. default is None) | |
| - `PIPELINE` - what elements to download? | |
| **Step 3: Run the download script** | |
| `python download_crowdsource_dataset.py` | |
| The script will create the following sub-directories in `SAVEPATH`: | |
| |_ annotations : where JSON annotations will be saves for each slide | |
| |_ masks : where the ground truth masks to use for training and validation are saved | |
| |_ images: where RGB images corresponding to masks are saved | |
| |_ wsis (legacy) : Ignore this. No longer supported. | |
| |_ logs : in case anythign goes wrong | |
| ------------------------------------------------- | |
| ## Licensing | |
| This dataset is licensed under a [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/). | |
| Please cite our paper if you use the data. | |