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Datasets:
corentinm7
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MyoQuant-SDH-Data

Tasks:
Image Classification
Size:
10K<n<100K
Tags:
myology
biology
histology
muscle
cells
fibers
License:
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metadata
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
annotations_creators:
  - expert-generated
language: []
language_creators:
  - expert-generated
license:
  - agpl-3.0
multilinguality: []
pretty_name: SDH staining muscle fiber histology images used to train MyoQuant model.
size_categories:
  - 10K<n<100K
source_datasets:
  - original
tags:
  - myology
  - biology
  - histology
  - muscle
  - cells
  - fibers
  - myopathy
  - SDH
  - myoquant
task_categories:
  - image-classification

Dataset Card for MyoQuant SDH Data

Table of Contents

Dataset Description

Dataset Summary

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:

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 DNM 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:

A second pass of verification was done by:

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, DNM) 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 Email: corentin.meyer@etu.unistra.fr Github: @lambda-science

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

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

Partners

Partner Banner

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