AssemblyNet-AD
AssemblyNet-AD is a pipeline designed to automatically detect/predict Alzheimer's Disease (AD) in a T1w brain MRI. It gets an anonymized MRI brain volume in NIFTI format and produces a pdf report containing two scores based on structure grading and structure atrophy.
Dementia Scores
Grading Score: First, AssemblyNet-AD produces a grading score based on deep learning models trained to discriminate AD signature. The grading map provides a score for each structure that reflects the similarity of the subject under study to a population with dementia (i.e., if the T1w MRI content presents AD-like patterns).
Atrophy Score: Second, AssemblyNet-AD produces an atrophy score called HAVAs based on lifespan modelling score. Using the volumes of hippocampus, amygdala and inferior lateral ventricle, HAVAs estimates the probability that the subject under study is closer to AD lifespan model than control lifespan model.
All the considered structures are segmented using AssemblyNet
Be aware that AssemblyNet-AD has been designed only to discriminate AD patients from controls. Please do not use it for other types of pathologies (e.g., Parkinson, Vascular dementia, ...).
Report
Once the process is finished you will be notified by e-mail so you will be able to download a package including some image files and two (CSV and PDF) reports gathering all the dementia scores values calculated.
References
H.-D. Nguyen, M. Clément, B. Mansencal, P. Coupé. Towards better interpretable and generalizable AD detection using collective artificial intelligence. Computerized Medical Imaging and Graphics (2023): 102171. PDF
P. Coupé, J. V. Manjón, B. Mansencal, T. Tourdias, G. Catheline, V. Planche. Hippocampal‐amygdalo‐ventricular atrophy score: Alzheimer disease detection using normative and pathological lifespan models. Human Brain Mapping 43, no. 10 (2022): 3270-3282.PDF.
P. Coupé, B. Mansencal, M. Clément, R. Giraud, B. Denis de Senneville, V.-T Ta, V. Lepetit, J. V. Manjon. AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation. NeuroImage, 219, 117026, 2020. PDF
de Senneville, B.D., Manjon, J.V. and Coupé, P., 2020. RegQCNET: Deep quality control for image-to-template brain MRI affine registration. Physics in Medicine & Biology, 65(22), p.225022. PDF