TY  - CONF
AU  - Schiffer, Christian
AU  - Amunts, Katrin
AU  - Harmeling, Stefan
AU  - Dickscheid, Timo
TI  - Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections
M1  - FZJ-2021-02931
SP  - 4
PY  - 2021
AB  - Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections. Recent work provided the first automatic segmentations of cytoarchitectonic areas in the visual system using Convolutional Neural Networks. We aim to extend this approach to become applicable to a wider range of brain areas, envisioning a solution for mapping the complete human brain. Inspired by recent success in image classification, we propose a contrastive learning objective for encoding microscopic image patches into robust microstructural features, which are efficient for cytoarchitectonic area classification. We show that a model pre-trained using this learning task outperforms a model trained from scratch, as well as a model pre-trained on a recently proposed auxiliary task. We perform cluster analysis in the feature space to show that the learned representations form anatomically meaningful groups.
T2  - 18th International Symposium on Biomedical Imaging (ISBI)
CY  - 13 Apr 2021 - 16 Apr 2021, Nice (France)
Y2  - 13 Apr 2021 - 16 Apr 2021
M2  - Nice, France
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - https://juser.fz-juelich.de/record/893921
ER  -