Welcome to the CADDementia Wiki
This is the Wiki page belonging to the challenge on computer-aided diagnosis of dementia based on structural MRI data (CADDementia).
For enabling reuse of the algorithms proposed in this challenge, participants are encouraged to write a practical guide on how to apply their algorithm on this wiki, where possible including an executable of their method.
The aim of this practical guide is to enable people to apply your algorithm to their data. This practical guide can be presented in different ways:
- providing an executable of the method
- providing a step by step guide
- referring to a paper or another website
Practical guide to the proposed algorithms
Algorithm for illustration:
- EE Bron, M Smits, JC van Swieten, WJ Niessen, S Klein (Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, the Netherlands) Dementia classification of AD, MCI and controls for the CADDementia dataset
Algorithms by participating teams:
- A Abdulkadir, J Peter, T Brox, O Ronneberger, S Kloeppel (Department of Psychiatry and Psychotherapy, University Medical Centre Freiburg, Germany) Voxel-based multi-class classification of AD, MCI, and elderly controls. Blind evaluation on an independent test set
- A Alansary, M Nitzken, A Black, A Elnakib, F Khalifa, K Stinebruner, A Soliman, M Mostapha, M Casanova, A El-Baz (BioImaging Laboratory, University of Louisville, USA) Brain Surface Analysis for Dementia Diagnosis
- N Amoroso, R Errico, R Bellotti (National Institute of Nuclear Physics, Branch of Bari, Italy) PRISMA-CAD: Fully automated method for Computer-Aided Diagnosis of Dementia based on structural MRI data
- D Cárdenas-Peña, A Álvarez-Meza, G Castellanos-Dominguez (Signal Processing and Recognition Group , Universidad Nacional de Colombia, Colombia) CADDementia based on structural MRI using Supervised Kernel based Representations
- CV Dolph, MD Samad, KM Iftekharuddin (Vision Lab, Old Dominion University, VA, USA) 2014 CADDementia Challenge
- SF Eskildsen, P Coupé, V Fonov, DL Collins (Center of Functionally Integrative Neuroscience, Aarhus University, Denmark) Detecting Alzheimer’s disease by morphological MRI using hippocampal grading and cortical thickness
- K Franke, C Gaser (Structural Brain Mapping Group, Departments of Neurology & Psychiatry, Jena University Hospital, Germany) Dementia classification based on brain age estimation
- C Ledig, R Guerrero, T Tong, K Gray, A Schmidt-Richberg, A Makropoulos, RA Heckemann, D Rueckert (Department of Computing, Imperial College London, UK) Alzheimer’s disease state classification using structural volumetry, cortical thickness and intensity features
- E Moradi, C Gaser, H Huttunen, J Tohka (Department of Signal Processing, Tampere University of Technology, Finland) MRI based dementia classification using semi-supervised learning and domain adaptation
- A Routier, P Gori, AB Graciano Fouquier, S Lecomte, O Colliot, S Durrleman (Sorbonne Universités, UPMC Université Paris 06, France) Evaluation of morphometric descriptors of deep brain structures for the automatic classification of patients with Alzheimer's disease, mild cognitive impairment and elderly controls
- A Sarica, G Di Fatta, G Smith, M Cannataro, D Saddy (Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy) Advanced Feature Selection in Multinominal Dementia Classification from Structural MRI Data
- F Sensi, L Rei, G Gemme, P Bosco, N Amoroso, A Chincarini (National Institute of Nuclear Physics, Branch of Genoa, Italy) Global Disease Index, a novel tool for MTL atrophy assessment
- GM Smith, ZV Stoyanov, DV Greetham, P Grindrod, JD Saddy (School of Systems Engineering, University of Reading, UK) Towards the Computer-aided Diagnosis of Dementia based on the Geometric and Network Connectivity of Structural MRI Data
- L Sørensen, A Pai, C Anker, I Balas, M Lillholm, C Igel, M Nielsen (Department of Computer Science, University of Copenhagen, Denmark) Dementia Diagnosis using MRI Cortical Thickness, Shape, Texture, and Volumetry
- S Tangaro, P Inglese, R Maglietta, A Tateo (National Institute of Nuclear Physics, Branch of Bari, Italy) MIND-BA: Fully automated method for Computer-Aided Diagnosis of Dementia based on structural MRI data
- C Wachinger, K Batmanghelich, P Golland, M Reuter (Computer Science and Artificial Intelligence Lab, MIT, MA, USA) BrainPrint in the Computer-Aided Diagnosis of Alzheimer's Disease
How does this work?
- You can make a practical guide for your algorithm by clicking on your algorithm in the list above and editing the page. See Template.
- You will receive a login for this Wiki after you submitted the results and workshop paper describing the proposed algorithm.
- You can change your password under Preferences (top right of the page).
If you have any question, please send an email to email@example.com