[](https://www.python.org/downloads/release/python-360/)
[](https://github.com/psf/black)
<img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=flat-square&logo=Pytorch&logoColor=white"/></a>
___
# PathoPatch:
## Accelerating Artificial Intelligence Based Whole Slide Image Analysis with an Optimized Preprocessing Pipeline
---
## Installation
1. Clone the repository:
2. Create a conda environment with Python 3.10.12 version and install conda requirements: `conda env create -f environment.yaml -vv`. You can change the environment name by editing the `name` tag in the environment.yaml file.
This step is necessary, as we need to install `Openslide` with binary files. This is easier with conda. Otherwise, installation from [source](https://openslide.org/api/python/) needs to be performed and packages installed with pi
3. Activate environment: `conda activate pathopatch_env`
4. **Optional: cuCIM**
Run `conda install -c rapidsai cucim` inside your conda environment. This process is time consuming, so you should be patient. Also follow their [official guideline](https://github.com/rapidsai/cucim) if any problems occur.
### Development
1. Install pre-commit with `pre-commit install`
## Usage
In our Pre-Processing pipeline, we are able to extract quadratic patches from detected tissue areas, load annotation files (`.json`) and apply color normlizations. We make use of the popular [OpenSlide](https://openslide.org/) library, but extended it with the [RAPIDS cuCIM](https://github.com/rapidsai/cucim) framework for a speedup in patch-extraction.
The CLI of the main script for patch extraction ([main_extraction](preprocessing/main_extraction.py)) is as follows:
```bash
python3 main_extraction.py [-h]
[--wsi_paths WSI_PATHS]
[--wsi_filelist WSI_FILELIST]
[--output_path OUTPUT_PATH]
[--wsi_extension {svs}]
[--config CONFIG]
[--patch_size PATCH_SIZE]
[--patch_overlap PATCH_OVERLAP]
[--target_mpp TARGET_MPP]
[--target_mag TARGET_MAG]
[--downsample DOWNSAMPLE]
[--level LEVEL]
[--context_scales [CONTEXT_SCALES ...]]
[--check_resolution CHECK_RESOLUTION]
[--processes PROCESSES]
[--overwrite]
[--annotation_paths ANNOTATION_PATHS]
[--annotation_extension {json,xml}]
[--incomplete_annotations]
[--label_map_file LABEL_MAP_FILE]
[--save_only_annotated_patches]
[--save_context_without_mask]
[--exclude_classes EXCLUDE_CLASSES]
[--store_masks]
[--overlapping_labels]
[--normalize_stains]
[--normalization_vector_json NORMALIZATION_VECTOR_JSON]
[--min_intersection_ratio MIN_INTERSECTION_RATIO]
[--tissue_annotation TISSUE_ANNOTATION]
[--tissue_annotation_intersection_ratio TISSUE_ANNOTATION_INTERSECTION_RATIO]
[--masked_otsu]
[--otsu_annotation OTSU_ANNOTATION]
[--filter_patches FILTER_PATCHES]
[--apply_prefilter APPLY_PREFILTER]
[--log_path LOG_PATH]
[--log_level {critical,error,warning,info,debug}]
[--hardware_selection {cucim,openslide}]
[--wsi_properties DICT]
optional arguments:
-h, --help show this help message and exit
--wsi_paths WSI_PATHS
Path to the folder where all WSI are stored or path to a single WSI-file. (default: None)
--wsi_filelist WSI_FILELIST
Path to a csv-filelist with WSI files (separator: `,`), if provided just these files are
used.Must include full paths to WSIs, including suffixes.Can be used as an replacement for
the wsi_paths option.If both are provided, yields an error. (default: None)
--output_path OUTPUT_PATH
Path to the folder where the resulting dataset should be stored. (default: None)
--wsi_extension {svs}
The extension types used for the WSI files, the options are: ['svs'] (default: None)
--config CONFIG Path to a config file. The config file can hold the same parameters as the CLI. Parameters
provided with the CLI are always having precedence over the parameters in the config file.
(default: None)
--patch_size PATCH_SIZE
The size of the patches in pixel that will be retrieved from the WSI, e.g. 256 for 256px
(default: None)
--patch_overlap PATCH_OVERLAP
The percentage amount pixels that should overlap between two different patches. Please
Provide as integer between 0 and 100, indicating overlap in percentage. (default: None)
--downsample DOWNSAMPLE
Each WSI level is downsampled by a factor of 2, downsample expresses which kind of
downsampling should be used with respect to the highest possible resolution. Medium
priority, gets overwritten by target_mag if provided, but overwrites level. (default:
None)
--target_mpp TARGET_MPP
If this parameter is provided, the output level of the WSI corresponds to the level that
is at the target microns per pixel of the WSI. Alternative to target_mag, downsaple and level.
Highest priority,
overwrites target_mag, downsample and level if provided. (default: None)
--target_mag TARGET_MAG
If this parameter is provided, the output level of the WSI corresponds to the level that
is at the target magnification of the WSI. Alternative to target_mpp, downsaple and level.
High priority, just target_mpp has a higher priority,
overwrites downsample and level if provided. (default: None)
--level LEVEL The tile level for sampling, alternative to downsample. Lowest priority, gets overwritten
by target_mag and downsample if they are provided. (default: None)
--context_scales [CONTEXT_SCALES ...]
Define context scales for context patches. Context patches are centered around a central
patch. The context-patch size is equal to the patch-size, but downsampling is different
(default: None)
--check_resolution CHECK_RESOLUTION
If a float value is supplies, the program checks whether the resolution of all images
corresponds to the given value (default: None)
--processes PROCESSES
The number of processes to use. (default: None)
--overwrite Overwrite the patches that have already been created in case they already exist. Removes
dataset. Handle with care! (default: None)
--annotation_paths ANNOTATION_PATHS
Path to the subfolder where the XML/JSON annotations are stored or path to a file
(default: None)
--annotation_extension {json,xml}
The extension types used for the annotation files, the options are: ['json', 'xml']
(default: None)
--incomplete_annotations
Set to allow WSI without annotation file (default: None)
--label_map_file LABEL_MAP_FILE
The path to a json file that contains the mapping between the annotation labels and some
integers; an example can be found in examples (default: None)
--save_only_annotated_patches
If true only patches containing annotations will be stored (default: None)
--save_context_without_mask
This is helpful for extracting patches, that are not within a mask, but needed for the
Valuing Vicinity Segmentation Algorithms. This flag is specifically helpful if only fully annotated
patches should be extracted from a region of interest and their masks are stored,
but also sourrounding neighbourhood patches are needed (default: False)
--exclude_classes EXCLUDE_CLASSES
Can be used to exclude annotation classes (default: None)
--store_masks Set to store masks per patch. Defaults to false (default: None)
--overlapping_labels Per default, labels (annotations) are mutually exclusive. If labels overlap, they are
overwritten according to the label_map.json ordering (highest number = highest priority
(default: None)
--normalize_stains Uses Macenko normalization on a portion of the whole slide image (default: None)
--normalization_vector_json NORMALIZATION_VECTOR_JSON
The path to a JSON file where the normalization vectors are stored (default: None)
--adjust_brightness Normalize brightness in a batch by clipping to 90 percent. Not recommended, but kept for legacy reasons (default: None)
--min_intersection_ratio MIN_INTERSECTION_RATIO
The minimum intersection between the tissue mask and the patch. Must be between 0 and 1. 0
means that all patches are extracted. (default: None)
--tissue_annotation TISSUE_ANNOTATION
Can be used to name a polygon annotation to determine the tissue area. If a tissue
annotation is provided, no Otsu-thresholding is performed (default: None)
--tissue_annotation_intersection_ratio TISSUE_ANNOTATION_INTERSECTION_RATIO
Intersection ratio with tissue annotation. Helpful, if ROI annotation is passed,
which should not interfere with background ratio. If not provided,
the default min_intersection_ratio with the background is used. (default: None)
--masked_otsu Use annotation to mask the thumbnail before otsu-thresholding is used (default: None)
--otsu_annotation OTSU_ANNOTATION
Can be used to name a polygon annotation to determine the area for masked otsu
thresholding. Seperate multiple labels with ' ' (whitespace) (default: None)
--filter_patches FILTER_PATCHES
Post-extraction patch filtering to sort out artefacts, marker and other non-tissue patches with a DL model. Time consuming.
(default: False)
--apply_prefilter APPLY_PREFILTER
Pre-extraction mask filtering to remove marker from mask before applying otsu
(default: False)
--log_path LOG_PATH Path where log files should be stored. Otherwise, log files are stored in the output
folder (default: None)
--log_level {critical,error,warning,info,debug}
Set the logging level. Options are ['critical', 'error', 'warning', 'info', 'debug']
(default: None)
--hardware_selection {cucim,openslide}
Select hardware device (just if available, otherwise always cucim). Defaults to cucim.)
--wsi_properties WSI_PROPERTIES
Can be used to pass the wsi properties manually, but just applies if metadata cannot be derived from OpenSlide (e.g., for .tiff files). Supported keys are slide_mpp and magnification
(default: None)
```
**Label-Map**:
An exemplary `label_map.json` file is shown below. It is important that the background label always has a 0 assigned as integer value
Example:
```json
{
"Background": 0,
"Tissue-Annotation": 1,
"Tumor": 2,
"Stroma": 3,
"Necrosis": 4
}
```
**Precedence of Target-Magnification, Downsampling and Level**
Target_mpp has the highest priority. If all four are passed, always the target mpp is used for output. Level has the lowest priority.
Sorted by priority:
- Target microns per pixel: Overwrites all other selections
- Target magnification: Overwrites downsampling and level
- Downsampling: Overwrites level
- Level: Lowest priority, default used when neither target magnification nor downsampling is passed
### CLI
A CLI is used to start the preprocessing. The entry-point is the [main_extraction.py](main_extraction.py) file. In addition to the CLI, also a configuration file can be passed via
```bash
python3 pathopatcher/main_extraction.py --config path/to/config.yaml
```
Exemplary configuration file: [patch_extraction.yaml](examples/patch_extraction.yaml)
### Resulting Dataset Structure
In general, the folder structure for a preprocessed dataset looks like this:
The aim of pre-processing is to create one dataset per WSI in the following structure:
```bash
WSI_Name
├── annotation_masks # thumbnails of extracted annotation masks
│ ├── all_overlaid.png # all with same dimension as the thumbnail
│ ├── tumor.png
│ └── ...
├── context # context patches, if extracted
│ ├── 2 # subfolder for each scale
│ │ ├── WSI_Name_row1_col1_context_2.png
│ │ ├── WSI_Name_row2_col1_context_2.png
│ │ └── ...
│ └── 4
│ │ ├── WSI_Name_row1_col1_context_2.png
│ │ ├── WSI_Name_row2_col1_context_2.png
│ │ └── ...
├── masks # Mask (numpy) files for each patch -> optional folder for segmentation
│ ├── WSI_Name_row1_col1.npy
│ ├── WSI_Name_row2_col1.npy
│ └── ...
├── metadata # Metadata files for each patch
│ ├── WSI_Name_row1_col1.yaml
│ ├── WSI_Name_row2_col1.yaml
│ └── ...
├── patches # Patches as .png files
│ ├── WSI_Name_row1_col1.png
│ ├── WSI_Name_row2_col1.png
│ └── ...
├── thumbnails # Different kind of thumbnails
│ ├── thumbnail_mpp_5.png
│ ├── thumbnail_downsample_32.png
│ └── ...
├── tissue_masks # Tissue mask images for checking
│ ├── mask.png # all with same dimension as the thumbnail
│ ├── mask_nogrid.png
│ └── tissue_grid.png
├── mask.png # tissue mask with green grid
├── metadata.yaml # WSI metdata for patch extraction
├── patch_metadata.json # Patch metadata of WSI merged in one file
└── thumbnail.png # WSI thumbnail
```
## License
<p xmlns:cc="http://creativecommons.org/ns#" xmlns:dct="http://purl.org/dc/terms/"><a property="dct:title" rel="cc:attributionURL" href="https://github.com/TIO-IKIM/PathoPatcher">PathoPatcher</a> by <a rel="cc:attributionURL dct:creator" property="cc:attributionName" href="https://github.com/FabianHoerst">Fabian Hörst, University Hospital Essen,</a> is licensed under <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">CC BY-NC-SA 4.0<img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1"><img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1"></a></p>
Raw data
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"name": "pathopatch",
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"author": "Fabian H\u00f6rst",
"author_email": "fabian.hoerst@uk-essen.de",
"download_url": "https://files.pythonhosted.org/packages/d8/8f/246eeb006e8628ab8cc4371428f55f0fed60bb4f655b127b66711d5b1ef2/pathopatch-1.0.4b0.tar.gz",
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"description": "[](https://www.python.org/downloads/release/python-360/)\n[](https://github.com/psf/black)\n\n<img src=\"https://img.shields.io/badge/PyTorch-EE4C2C?style=flat-square&logo=Pytorch&logoColor=white\"/></a>\n\n___\n\n# PathoPatch:\n## Accelerating Artificial Intelligence Based Whole Slide Image Analysis with an Optimized Preprocessing Pipeline\n---\n\n## Installation\n\n1. Clone the repository:\n2. Create a conda environment with Python 3.10.12 version and install conda requirements: `conda env create -f environment.yaml -vv`. You can change the environment name by editing the `name` tag in the environment.yaml file.\nThis step is necessary, as we need to install `Openslide` with binary files. This is easier with conda. Otherwise, installation from [source](https://openslide.org/api/python/) needs to be performed and packages installed with pi\n3. Activate environment: `conda activate pathopatch_env`\n4. **Optional: cuCIM**\nRun `conda install -c rapidsai cucim` inside your conda environment. This process is time consuming, so you should be patient. Also follow their [official guideline](https://github.com/rapidsai/cucim) if any problems occur.\n\n### Development\n1. Install pre-commit with `pre-commit install`\n\n\n## Usage\nIn our Pre-Processing pipeline, we are able to extract quadratic patches from detected tissue areas, load annotation files (`.json`) and apply color normlizations. We make use of the popular [OpenSlide](https://openslide.org/) library, but extended it with the [RAPIDS cuCIM](https://github.com/rapidsai/cucim) framework for a speedup in patch-extraction.\n\n\nThe CLI of the main script for patch extraction ([main_extraction](preprocessing/main_extraction.py)) is as follows:\n\n```bash\npython3 main_extraction.py [-h]\n [--wsi_paths WSI_PATHS]\n [--wsi_filelist WSI_FILELIST]\n [--output_path OUTPUT_PATH]\n [--wsi_extension {svs}]\n [--config CONFIG]\n [--patch_size PATCH_SIZE]\n [--patch_overlap PATCH_OVERLAP]\n [--target_mpp TARGET_MPP]\n [--target_mag TARGET_MAG]\n [--downsample DOWNSAMPLE]\n [--level LEVEL]\n [--context_scales [CONTEXT_SCALES ...]]\n [--check_resolution CHECK_RESOLUTION]\n [--processes PROCESSES]\n [--overwrite]\n [--annotation_paths ANNOTATION_PATHS]\n [--annotation_extension {json,xml}]\n [--incomplete_annotations]\n [--label_map_file LABEL_MAP_FILE]\n [--save_only_annotated_patches]\n [--save_context_without_mask]\n [--exclude_classes EXCLUDE_CLASSES]\n [--store_masks]\n [--overlapping_labels]\n [--normalize_stains]\n [--normalization_vector_json NORMALIZATION_VECTOR_JSON]\n [--min_intersection_ratio MIN_INTERSECTION_RATIO]\n [--tissue_annotation TISSUE_ANNOTATION]\n [--tissue_annotation_intersection_ratio TISSUE_ANNOTATION_INTERSECTION_RATIO]\n [--masked_otsu]\n [--otsu_annotation OTSU_ANNOTATION]\n [--filter_patches FILTER_PATCHES]\n [--apply_prefilter APPLY_PREFILTER]\n [--log_path LOG_PATH]\n [--log_level {critical,error,warning,info,debug}]\n [--hardware_selection {cucim,openslide}]\n [--wsi_properties DICT]\n\noptional arguments:\n -h, --help show this help message and exit\n --wsi_paths WSI_PATHS\n Path to the folder where all WSI are stored or path to a single WSI-file. (default: None)\n --wsi_filelist WSI_FILELIST\n Path to a csv-filelist with WSI files (separator: `,`), if provided just these files are\n used.Must include full paths to WSIs, including suffixes.Can be used as an replacement for\n the wsi_paths option.If both are provided, yields an error. (default: None)\n --output_path OUTPUT_PATH\n Path to the folder where the resulting dataset should be stored. (default: None)\n --wsi_extension {svs}\n The extension types used for the WSI files, the options are: ['svs'] (default: None)\n --config CONFIG Path to a config file. The config file can hold the same parameters as the CLI. Parameters\n provided with the CLI are always having precedence over the parameters in the config file.\n (default: None)\n --patch_size PATCH_SIZE\n The size of the patches in pixel that will be retrieved from the WSI, e.g. 256 for 256px\n (default: None)\n --patch_overlap PATCH_OVERLAP\n The percentage amount pixels that should overlap between two different patches. Please\n Provide as integer between 0 and 100, indicating overlap in percentage. (default: None)\n --downsample DOWNSAMPLE\n Each WSI level is downsampled by a factor of 2, downsample expresses which kind of\n downsampling should be used with respect to the highest possible resolution. Medium\n priority, gets overwritten by target_mag if provided, but overwrites level. (default:\n None)\n --target_mpp TARGET_MPP\n If this parameter is provided, the output level of the WSI corresponds to the level that\n is at the target microns per pixel of the WSI. Alternative to target_mag, downsaple and level.\n Highest priority,\n overwrites target_mag, downsample and level if provided. (default: None)\n --target_mag TARGET_MAG\n If this parameter is provided, the output level of the WSI corresponds to the level that\n is at the target magnification of the WSI. Alternative to target_mpp, downsaple and level.\n High priority, just target_mpp has a higher priority,\n overwrites downsample and level if provided. (default: None)\n --level LEVEL The tile level for sampling, alternative to downsample. Lowest priority, gets overwritten\n by target_mag and downsample if they are provided. (default: None)\n --context_scales [CONTEXT_SCALES ...]\n Define context scales for context patches. Context patches are centered around a central\n patch. The context-patch size is equal to the patch-size, but downsampling is different\n (default: None)\n --check_resolution CHECK_RESOLUTION\n If a float value is supplies, the program checks whether the resolution of all images\n corresponds to the given value (default: None)\n --processes PROCESSES\n The number of processes to use. (default: None)\n --overwrite Overwrite the patches that have already been created in case they already exist. Removes\n dataset. Handle with care! (default: None)\n --annotation_paths ANNOTATION_PATHS\n Path to the subfolder where the XML/JSON annotations are stored or path to a file\n (default: None)\n --annotation_extension {json,xml}\n The extension types used for the annotation files, the options are: ['json', 'xml']\n (default: None)\n --incomplete_annotations\n Set to allow WSI without annotation file (default: None)\n --label_map_file LABEL_MAP_FILE\n The path to a json file that contains the mapping between the annotation labels and some\n integers; an example can be found in examples (default: None)\n --save_only_annotated_patches\n If true only patches containing annotations will be stored (default: None)\n --save_context_without_mask\n This is helpful for extracting patches, that are not within a mask, but needed for the\n Valuing Vicinity Segmentation Algorithms. This flag is specifically helpful if only fully annotated\n patches should be extracted from a region of interest and their masks are stored,\n but also sourrounding neighbourhood patches are needed (default: False)\n --exclude_classes EXCLUDE_CLASSES\n Can be used to exclude annotation classes (default: None)\n --store_masks Set to store masks per patch. Defaults to false (default: None)\n --overlapping_labels Per default, labels (annotations) are mutually exclusive. If labels overlap, they are\n overwritten according to the label_map.json ordering (highest number = highest priority\n (default: None)\n --normalize_stains Uses Macenko normalization on a portion of the whole slide image (default: None)\n --normalization_vector_json NORMALIZATION_VECTOR_JSON\n The path to a JSON file where the normalization vectors are stored (default: None)\n --adjust_brightness Normalize brightness in a batch by clipping to 90 percent. Not recommended, but kept for legacy reasons (default: None)\n --min_intersection_ratio MIN_INTERSECTION_RATIO\n The minimum intersection between the tissue mask and the patch. Must be between 0 and 1. 0\n means that all patches are extracted. (default: None)\n --tissue_annotation TISSUE_ANNOTATION\n Can be used to name a polygon annotation to determine the tissue area. If a tissue\n annotation is provided, no Otsu-thresholding is performed (default: None)\n --tissue_annotation_intersection_ratio TISSUE_ANNOTATION_INTERSECTION_RATIO\n Intersection ratio with tissue annotation. Helpful, if ROI annotation is passed,\n which should not interfere with background ratio. If not provided,\n the default min_intersection_ratio with the background is used. (default: None)\n --masked_otsu Use annotation to mask the thumbnail before otsu-thresholding is used (default: None)\n --otsu_annotation OTSU_ANNOTATION\n Can be used to name a polygon annotation to determine the area for masked otsu\n thresholding. Seperate multiple labels with ' ' (whitespace) (default: None)\n --filter_patches FILTER_PATCHES\n Post-extraction patch filtering to sort out artefacts, marker and other non-tissue patches with a DL model. Time consuming.\n (default: False)\n --apply_prefilter APPLY_PREFILTER\n Pre-extraction mask filtering to remove marker from mask before applying otsu\n (default: False)\n --log_path LOG_PATH Path where log files should be stored. Otherwise, log files are stored in the output\n folder (default: None)\n --log_level {critical,error,warning,info,debug}\n Set the logging level. Options are ['critical', 'error', 'warning', 'info', 'debug']\n (default: None)\n --hardware_selection {cucim,openslide}\n Select hardware device (just if available, otherwise always cucim). Defaults to cucim.)\n --wsi_properties WSI_PROPERTIES\n Can be used to pass the wsi properties manually, but just applies if metadata cannot be derived from OpenSlide (e.g., for .tiff files). Supported keys are slide_mpp and magnification\n (default: None)\n```\n\n**Label-Map**:\n\nAn exemplary `label_map.json` file is shown below. It is important that the background label always has a 0 assigned as integer value\n\nExample:\n```json\n{\n \"Background\": 0,\n \"Tissue-Annotation\": 1,\n \"Tumor\": 2,\n \"Stroma\": 3,\n \"Necrosis\": 4\n}\n```\n**Precedence of Target-Magnification, Downsampling and Level**\n\nTarget_mpp has the highest priority. If all four are passed, always the target mpp is used for output. Level has the lowest priority.\nSorted by priority:\n\n- Target microns per pixel: Overwrites all other selections\n- Target magnification: Overwrites downsampling and level\n- Downsampling: Overwrites level\n- Level: Lowest priority, default used when neither target magnification nor downsampling is passed\n\n### CLI\n\nA CLI is used to start the preprocessing. The entry-point is the [main_extraction.py](main_extraction.py) file. In addition to the CLI, also a configuration file can be passed via\n```bash\npython3 pathopatcher/main_extraction.py --config path/to/config.yaml\n```\nExemplary configuration file: [patch_extraction.yaml](examples/patch_extraction.yaml)\n\n\n\n### Resulting Dataset Structure\nIn general, the folder structure for a preprocessed dataset looks like this:\nThe aim of pre-processing is to create one dataset per WSI in the following structure:\n```bash\nWSI_Name\n\u251c\u2500\u2500 annotation_masks # thumbnails of extracted annotation masks\n\u2502 \u251c\u2500\u2500 all_overlaid.png # all with same dimension as the thumbnail\n\u2502 \u251c\u2500\u2500 tumor.png\n\u2502 \u2514\u2500\u2500 ... \n\u251c\u2500\u2500 context # context patches, if extracted\n\u2502 \u251c\u2500\u2500 2 # subfolder for each scale\n\u2502 \u2502 \u251c\u2500\u2500 WSI_Name_row1_col1_context_2.png\n\u2502 \u2502 \u251c\u2500\u2500 WSI_Name_row2_col1_context_2.png\n\u2502 \u2502 \u2514\u2500\u2500 ...\n\u2502 \u2514\u2500\u2500 4\n\u2502 \u2502 \u251c\u2500\u2500 WSI_Name_row1_col1_context_2.png\n\u2502 \u2502 \u251c\u2500\u2500 WSI_Name_row2_col1_context_2.png\n\u2502 \u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 masks # Mask (numpy) files for each patch -> optional folder for segmentation\n\u2502 \u251c\u2500\u2500 WSI_Name_row1_col1.npy\n\u2502 \u251c\u2500\u2500 WSI_Name_row2_col1.npy\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 metadata # Metadata files for each patch\n\u2502 \u251c\u2500\u2500 WSI_Name_row1_col1.yaml\n\u2502 \u251c\u2500\u2500 WSI_Name_row2_col1.yaml\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 patches # Patches as .png files\n\u2502 \u251c\u2500\u2500 WSI_Name_row1_col1.png\n\u2502 \u251c\u2500\u2500 WSI_Name_row2_col1.png\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 thumbnails # Different kind of thumbnails\n\u2502 \u251c\u2500\u2500 thumbnail_mpp_5.png\n\u2502 \u251c\u2500\u2500 thumbnail_downsample_32.png\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 tissue_masks # Tissue mask images for checking\n\u2502 \u251c\u2500\u2500 mask.png # all with same dimension as the thumbnail\n\u2502 \u251c\u2500\u2500 mask_nogrid.png\n\u2502 \u2514\u2500\u2500 tissue_grid.png\n\u251c\u2500\u2500 mask.png # tissue mask with green grid \n\u251c\u2500\u2500 metadata.yaml # WSI metdata for patch extraction\n\u251c\u2500\u2500 patch_metadata.json # Patch metadata of WSI merged in one file\n\u2514\u2500\u2500 thumbnail.png # WSI thumbnail\n```\n\n## License\n<p xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dct=\"http://purl.org/dc/terms/\"><a property=\"dct:title\" rel=\"cc:attributionURL\" href=\"https://github.com/TIO-IKIM/PathoPatcher\">PathoPatcher</a> by <a rel=\"cc:attributionURL dct:creator\" property=\"cc:attributionName\" href=\"https://github.com/FabianHoerst\">Fabian H\u00f6rst, University Hospital Essen,</a> is licensed under <a href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/?ref=chooser-v1\" target=\"_blank\" rel=\"license noopener noreferrer\" style=\"display:inline-block;\">CC BY-NC-SA 4.0<img style=\"height:22px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1\"><img style=\"height:22px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1\"><img style=\"height:22px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/nc.svg?ref=chooser-v1\"><img style=\"height:22px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/sa.svg?ref=chooser-v1\"></a></p>\n",
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