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Dataset assetOpen Source CommunityMedical Imaging AnalysisMultiple Sclerosis

SCLEROSIS, ISBI 2015, ljubljana, MICCAI 2008, MICCAI 2016, HEALTHY, KIRBY, OASIS-3

This dataset comprises various MRI images for automated multiple‑sclerosis (MS) diagnosis. It includes: - **SCLEROSIS**: 109 images. - **ISBI 2015** challenge: 21 images for longitudinal MS lesion segmentation. - **Ljubljana**: 30 training images. - **MICCAI 2008**: 51 images for the MS lesion segmentation challenge. - **MICCAI 2016**: 15 training images. - **HEALTHY**: 21 healthy subject images. - **KIRBY**: 42 images for multimodal MRI reproducibility. - **OASIS‑3**: forthcoming. The dataset provides detailed demographic information for several subsets (age, sex, MS subtype, etc.) and includes download instructions and preprocessing protocols such as rigid registration, MNI alignment, anisotropic filtering, skull stripping, and bias field correction.

Source
github
Created
Dec 8, 2019
Updated
Jul 4, 2023
Signals
322 views
Availability
Linked source ready
Overview

Dataset description and usage context

Dataset Overview

Dataset Content

  • SCLEROSIS: 109 images
  • ISBI 2015: 21 images, training set with 5 patients, test set with 14 patients
  • Ljubljana: 30 images, training only, 30 patients
    • Demographic Features:
      • Age
      • Sex (7 male, 23 female)
      • MS type (24 relapsing‑remitting, 2 clinically isolated syndrome, 1 primary progressive, 2 secondary progressive, 1 undiagnosed)
  • MICCAI 2008: 51 images, training set with 20 patients, test set with 25 patients, no additional attributes
  • MICCAI 2016: 15 images, training only, 15 patients
    • Demographic Features:
      • Age
      • Sex (7 male, 8 female)
  • HEALTHY: 21 images
  • KIRBY: 42 images, training set with 21 patients
    • Demographic Features:
      • Age
      • Sex (10 male, 11 female)
  • OASIS‑3: forthcoming

Dataset Download Instructions

  • ISBI 2015: Register and download here
  • Ljubljana: Download here
  • MICCAI 2008: Register and download here
  • MICCAI 2016: Register and download here
  • KIRBY: Download here
  • OASIS‑3: Register and download here

Dataset Pre‑processing Protocol

  • Pre‑processing Steps:
    • Rigid registration to T1 space
    • Alignment to MNI template
    • Anisotropic filtering
    • Skull stripping
    • Bias‑field (intensity non‑uniformity) correction

Dataset Preparation Workflow

  1. Run build_directory.py to create input/output folder structures.
  2. Download images and unzip into the appropriate folders.
  3. Execute change_extensions.py to convert images to .nii.gz format.
  4. Run create_json.py to generate a JSON dictionary of image paths.
  5. Execute main.py for image pre‑processing.
  6. Run Manipulating_data.py to split JSON data into training/testing sets (75 %/25 %), ensuring balanced representation of healthy and MS subjects.
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