
# Dataset Card for MyoQuant SDH Data

## Table of Contents

- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances and Splits](#data-instances-and-splits)
- [Dataset Creation and Annotations](#dataset-creation-and-annotations)
  - [Source Data and annotation process](#source-data-and-annotation-process)
  - [Who are the annotators ?](#who-are-the-annotators)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases and Limitations](#discussion-of-biases-and-limitations)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
- [The Team Behind this Dataset](#the-team-behind-this-dataset)
- [Partners](#partners)

## Dataset Description

- **Homepage:** https://github.com/lambda-science/MyoQuant
- **Repository:** https://huggingface.co/corentinm7/MyoQuant-SDH-Model
- **Paper:** Yet To Come
- **Leaderboard:** N/A
- **Point of Contact:** [**Corentin Meyer**, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://cmeyer.fr) email: <corentin.meyer@etu.unistra.fr>

### Dataset Summary

<p align="center">
  <img src="https://i.imgur.com/mzALgZL.png" alt="MyoQuant Banner" style="border-radius: 25px;" />
</p>

This dataset contains images of individual muscle fiber used to train [MyoQuant](https://github.com/lambda-science/MyoQuant) SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.

## Dataset Structure

### Data Instances and Splits

A total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set.  
See the table for the exact count of images in each category:  
| | Train (72%) | Validation (8%) | Test (20%) | TOTAL |
|---------|-------------|-----------------|------------|-------------|
| control | 9165 | 1019 | 2546 | 12730 (76%) |
| sick | 2920 | 325 | 812 | 4057 (24%) |
| TOTAL | 12085 | 1344 | 3358 | 16787 |

## Dataset Creation and Annotations

### Source Data and annotation process

To create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides.

### Who are the annotators?

All data in this dataset were generated and manually annotated by two experts:

- [**Quentin GIRAUD, PhD Student**](https://twitter.com/GiraudGiraud20) @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <quentin.giraud@igbmc.fr>
- **Charlotte GINESTE, Post-Doc** @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <charlotte.gineste@igbmc.fr>

A second pass of verification was done by:

- **Bertrand VERNAY, Platform Leader** @ [Light Microscopy Facility, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy), 1 rue Laurent Fries, 67404 Illkirch, France <bertrand.vernay@igbmc.fr>

### Personal and Sensitive Information

All image data comes from mice, there is no personal nor sensitive information in this dataset.

## Considerations for Using the Data

### Social Impact of Dataset

The aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images.

### Discussion of Biases and Limitations

This dataset has several limitations (non-exhaustive list):

- The images are from mice and thus might not be ideal to represent actual mechanism in human muscle
- The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.
- Only mitochondria anomaly was considered to classify cells as "sick", other anomalies were not considered, thus control cells might present other anomalies (such as what is called "cores" in congenital myopathies for examples)

## Additional Information

### Licensing Information

This dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.

### Citation Information

MyoQuant publication with model and data is yet to come.

## The Team Behind this Dataset

**The creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:**
- **[Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://cmeyer.fr) Email: <corentin.meyer@etu.unistra.fr> Github: [@lambda-science](https://github.com/lambda-science)**

Special thanks to the experts that created the data for this dataset and all the time they spend counting cells :

- **Quentin GIRAUD, PhD Student** @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <quentin.giraud@igbmc.fr>
- **Charlotte GINESTE, Post-Doc** @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <charlotte.gineste@igbmc.fr>

Last but not least thanks to Bertrand Vernay being at the origin of this project:

- **Bertrand VERNAY, Platform Leader** @ [Light Microscopy Facility, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy), 1 rue Laurent Fries, 67404 Illkirch, France <bertrand.vernay@igbmc.fr>

## Partners

<p align="center">
  <img src="https://i.imgur.com/m5OGthE.png" alt="Partner Banner" style="border-radius: 25px;" />
</p>

MyoQuant-SDH-Data is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte

task_categories:image-classification annotations_creators:expert-generated language_creators:expert-generated size_categories:10K<n<100K source_datasets:original license:agpl-3.0 myology biology histology muscle cells fibers myopathy SDH myoquant region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": [], "license": ["agpl-3.0"], "multilinguality": [], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["image-classification"], "pretty_name": "SDH staining muscle fiber histology images used to train MyoQuant model.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "control", "1": "sick"}}}}], "config_name": "SDH_16k", "splits": [{"name": "test", "num_bytes": 683067, "num_examples": 3358}, {"name": "train", "num_bytes": 2466024, "num_examples": 12085}, {"name": "validation", "num_bytes": 281243, "num_examples": 1344}], "download_size": 2257836789, "dataset_size": 3430334}, "tags": ["myology", "biology", "histology", "muscle", "cells", "fibers", "myopathy", "SDH", "myoquant"]}
TAGS
#task_categories-image-classification #annotations_creators-expert-generated #language_creators-expert-generated #size_categories-10K<n<100K #source_datasets-original #license-agpl-3.0 #myology #biology #histology #muscle #cells #fibers #myopathy #SDH #myoquant #region-us 

Dataset Card for MyoQuant SDH Data
==================================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
* Dataset Structure
	+ Data Instances and Splits
* Dataset Creation and Annotations
	+ Source Data and annotation process
	+ Who are the annotators ?
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases and Limitations
* Additional Information
	+ Licensing Information
	+ Citation Information
* The Team Behind this Dataset
* Partners


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper: Yet To Come
* Leaderboard: N/A
* Point of Contact: Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra email: [URL@URL](mailto:URL@URL)


### Dataset Summary



![](https://i.URL alt=)



This dataset contains images of individual muscle fiber used to train MyoQuant SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.


Dataset Structure
-----------------


### Data Instances and Splits


A total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set.  

See the table for the exact count of images in each category:



Dataset Creation and Annotations
--------------------------------


### Source Data and annotation process


To create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides.


### Who are the annotators?


All data in this dataset were generated and manually annotated by two experts:


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


A second pass of verification was done by:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)


### Personal and Sensitive Information


All image data comes from mice, there is no personal nor sensitive information in this dataset.


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


The aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images.


### Discussion of Biases and Limitations


This dataset has several limitations (non-exhaustive list):


* The images are from mice and thus might not be ideal to represent actual mechanism in human muscle
* The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.
* Only mitochondria anomaly was considered to classify cells as "sick", other anomalies were not considered, thus control cells might present other anomalies (such as what is called "cores" in congenital myopathies for examples)


Additional Information
----------------------


### Licensing Information


This dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.


MyoQuant publication with model and data is yet to come.


The Team Behind this Dataset
----------------------------


The creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:


* Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra Email: [URL@URL](mailto:URL@URL) Github: @lambda-science


Special thanks to the experts that created the data for this dataset and all the time they spend counting cells :


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


Last but not least thanks to Bertrand Vernay being at the origin of this project:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)


Partners
--------



![](https://i.URL alt=)



MyoQuant-SDH-Data is born within the collaboration between the CSTB Team @ ICube led by Julie D. Thompson, the Morphological Unit of the Institute of Myology of Paris led by Teresinha Evangelista, the imagery platform MyoImage of Center of Research in Myology led by Bruno Cadot, the photonic microscopy platform of the IGMBC led by Bertrand Vernay and the Pathophysiology of neuromuscular diseases team @ IGBMC led by Jocelyn Laporte



### Dataset Summary



![](https://i.URL alt=)



This dataset contains images of individual muscle fiber used to train MyoQuant SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.


Dataset Structure
----------------- ### Data Instances and Splits


A total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set.  

See the table for the exact count of images in each category:



Dataset Creation and Annotations
-------------------------------- ### Source Data and annotation process


To create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides. ### Who are the annotators?


All data in this dataset were generated and manually annotated by two experts:


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


A second pass of verification was done by:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL) ### Personal and Sensitive Information


All image data comes from mice, there is no personal nor sensitive information in this dataset.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images. ### Discussion of Biases and Limitations


This dataset has several limitations (non-exhaustive list):


* The images are from mice and thus might not be ideal to represent actual mechanism in human muscle
* The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.
* Only mitochondria anomaly was considered to classify cells as "sick", other anomalies were not considered, thus control cells might present other anomalies (such as what is called "cores" in congenital myopathies for examples)


Additional Information
---------------------- ### Licensing Information


This dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.


MyoQuant publication with model and data is yet to come.


The Team Behind this Dataset
----------------------------


The creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:


* Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra Email: [URL@URL](mailto:URL@URL) Github: @lambda-science


Special thanks to the experts that created the data for this dataset and all the time they spend counting cells :


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


Last but not least thanks to Bertrand Vernay being at the origin of this project:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)


Partners
--------



![](https://i.URL alt=)



MyoQuant-SDH-Data is born within the collaboration between the CSTB Team @ ICube led by Julie D. Thompson, the Morphological Unit of the Institute of Myology of Paris led by Teresinha Evangelista, the imagery platform MyoImage of Center of Research in Myology led by Bruno Cadot, the photonic microscopy platform of the IGMBC led by Bertrand Vernay and the Pathophysiology of neuromuscular diseases team @ IGBMC led by Jocelyn Laporte
TAGS
#task_categories-image-classification #annotations_creators-expert-generated #language_creators-expert-generated #size_categories-10K<n<100K #source_datasets-original #license-agpl-3.0 #myology #biology #histology #muscle #cells #fibers #myopathy #SDH #myoquant #region-us 
 ### Dataset Summary



![](https://i.URL alt=)



This dataset contains images of individual muscle fiber used to train MyoQuant SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.


Dataset Structure
----------------- ### Data Instances and Splits


A total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set.  

See the table for the exact count of images in each category:



Dataset Creation and Annotations
-------------------------------- ### Source Data and annotation process


To create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides. ### Who are the annotators?


All data in this dataset were generated and manually annotated by two experts:


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


A second pass of verification was done by:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL) ### Personal and Sensitive Information


All image data comes from mice, there is no personal nor sensitive information in this dataset.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


The aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images. ### Discussion of Biases and Limitations


This dataset has several limitations (non-exhaustive list):


* The images are from mice and thus might not be ideal to represent actual mechanism in human muscle
* The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.
* Only mitochondria anomaly was considered to classify cells as "sick", other anomalies were not considered, thus control cells might present other anomalies (such as what is called "cores" in congenital myopathies for examples)


Additional Information
---------------------- ### Licensing Information


This dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.


MyoQuant publication with model and data is yet to come.


The Team Behind this Dataset
----------------------------


The creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:


* Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra Email: [URL@URL](mailto:URL@URL) Github: @lambda-science


Special thanks to the experts that created the data for this dataset and all the time they spend counting cells :


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


Last but not least thanks to Bertrand Vernay being at the origin of this project:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)


Partners
--------



![](https://i.URL alt=)



MyoQuant-SDH-Data is born within the collaboration between the CSTB Team @ ICube led by Julie D. Thompson, the Morphological Unit of the Institute of Myology of Paris led by Teresinha Evangelista, the imagery platform MyoImage of Center of Research in Myology led by Bruno Cadot, the photonic microscopy platform of the IGMBC led by Bertrand Vernay and the Pathophysiology of neuromuscular diseases team @ IGBMC led by Jocelyn Laporte
passage: TAGS
#task_categories-image-classification #annotations_creators-expert-generated #language_creators-expert-generated #size_categories-10K<n<100K #source_datasets-original #license-agpl-3.0 #myology #biology #histology #muscle #cells #fibers #myopathy #SDH #myoquant #region-us 
### Dataset Summary



![](https://i.URL alt=)



This dataset contains images of individual muscle fiber used to train MyoQuant SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.


Dataset Structure
-----------------### Data Instances and Splits


A total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set.  

See the table for the exact count of images in each category:



Dataset Creation and Annotations
--------------------------------### Source Data and annotation process


To create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides. passage: ### Who are the annotators?


All data in this dataset were generated and manually annotated by two experts:


* Quentin GIRAUD, PhD Student @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)
* Charlotte GINESTE, Post-Doc @ Department Translational Medicine, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [charlotte.gineste@URL](mailto:charlotte.gineste@URL)


A second pass of verification was done by:


* Bertrand VERNAY, Platform Leader @ Light Microscopy Facility, IGBMC, CNRS UMR 7104, 1 rue Laurent Fries, 67404 Illkirch, France [URL@URL](mailto:URL@URL)### Personal and Sensitive Information


All image data comes from mice, there is no personal nor sensitive information in this dataset.


Considerations for Using the Data
---------------------------------### Social Impact of Dataset


The aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images.### Discussion of Biases and Limitations


This dataset has several limitations (non-exhaustive list):


* The images are from mice and thus might not be ideal to represent actual mechanism in human muscle
* The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.
* Only mitochondria anomaly was considered to classify cells as "sick", other anomalies were not considered, thus control cells might present other anomalies (such as what is called "cores" in congenital myopathies for examples)


Additional Information
----------------------