2024-01-23-li24b.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Joint cortical registration of geometry and function using semi-supervised learning	Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	li24b	0	Joint cortical registration of geometry and function using semi-supervised learning	862	876	862-876	862	false	Li, Jian and Tuckute, Greta and Fedorenko, Evelina and Edlow, Brian L and Fischl, Bruce and Dalca, Adrian V	given family Jian Li given family Greta Tuckute given family Evelina Fedorenko given family Brian L Edlow given family Bruce Fischl given family Adrian V Dalca	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/li24b/li24b.pdf