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Information Systems and Applied Computer Sciences

Chair of Explainable Machine Learning

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  5. Applied Computer Science
  6. Chair of Explainable Machine Learning
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  8. Dataset: MALPEM-ADNI: Features, binary masks, segmentations for 5074 ADNI subjects

Dataset: MALPEM-ADNI: Features, binary masks, segmentations for 5074 ADNI subjects

We employed a recently validated method (MALPEM) for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months.

The following is a summary of the publicly available dataset. For more details please refer to the [doi] and the README of the current GIN repository.

Processed Images from the ADNI cohort

  • List of all 5074 images (baseline and follow up images, incl. VISCODE)
  • List of 1069 baseline images (incl. disease label) used for analysis 
  • List of 802 baseline and m12 follow up image pairs (incl. disease label) used for analysis
  • List of 532 baseline and m24 follow up image pairs (incl. disease label) used for analysis

Features

  • All extracted cross-sectional (structural volumes, asymmetry) and longitudinal (volume change rate) features and selected clinical information (e.g. disease labels). Note: Not all of those features have been used in the manuscript. Please, refer to the paper for details.

Segmentations / Brain masks

  • 5074 cross-sectional structural segmentations in 138 distinct anatomical regions (calculated with MALPEM)
  • 5074 binary brain masks (calculated with pincram). Masks have been quality checked (baseline: visual, followup: automatic) 
  • Lookup table for all segmented 138 brain structures

(please cite for biomarker analysis) 
C. Ledig, A. Schuh, R. Guerrero, R. A. Heckemann and D. Rueckert, “Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database”, Scientific Reports, 8, 2018. [doi] [pdf] [bib] 

(please cite for dataset) 
C. Ledig, A. Schuh, R. Guerrero, R. A. Heckemann and D. Rueckert, “Dataset - Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database”,  G-Node, 2018. [dataset] [bib]