Influence of the Training Library Composition on a Patch-based label fusion method: Application to Hippocampus Segmentation on ADNI dataset.

Pierrick Coupé (1) , Vladimir Fonov (1) , Simon F. Eskildsen (1, 2) , José V. Manjón (3) , Douglas Arnold (1) , Louis D. Collins (1)

1 MNI - McConnell Brain Imaging Centre
2 AAU - Aalborg University [Denmark]
3 ITACA

The atrophy of medial temporal lobe structures is may serve as early biomarkers of Alzheimer's disease. The evaluation of hippocampus (HC) atrophy is estimated by volumetric studies requiring a segmentation step that can be very time consuming when done manually. This limitation can be overcome by using automatic segmentation methods. In this study, we propose to validate our nonlocal patch-based method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database by segmenting the HC of Cognitively Normal (CN) subjects and patients with early Alzheimer's Disease (AD).

Mots clés: Alzheimer's disease; hippocmapus; segmentation; brain; neuroimaging; nonlocal means

Domaines: Imagerie médicale; Neurosciences [q-bio.NC]; Ingénierie biomédicale; Traitement des images [eess.IV]; Neurosciences

Pierrick Coupé, Vladimir Fonov, Simon F. Eskildsen, José V. Manjón, Douglas Arnold, et al.. Influence of the Training Library Composition on a Patch-based label fusion method: Application to Hippocampus Segmentation on ADNI dataset.. Alzheimer's Association International Conference, Jul 2011, France. pp.S24. ⟨hal-00645526⟩