2024-01-23-gu24b.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
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs	Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lower resolution. A potential remedy to this issue lies in the fact that often multiple sequences are acquired on different planes. However, in practice, these sequences are not orthogonal to each other, limiting the applicability of many previous solutions to reconstruct higher-resolution images from multiple lower-resolution ones. We propose a novel deep learning-based solution to generating high-resolution masks from multiple low-resolution images. Our method combines segmentation and unsupervised registration networks by introducing two new regularizations to make registration and segmentation reinforce each other. Finally, we introduce a multi-view fusion method to generate high-resolution target object masks. The experimental results on two datasets show the superiority of our methods. Importantly, the advantage of not using high-resolution images in the training process makes our method applicable to a wide variety of MRI segmentation tasks.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	gu24b	0	SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs	119	133	119-133	119	false	Gu, Hanxue and He, Hongyu and Colglazier, Roy and Axelrod, Jordan and French, Robert and Mazurowski, Maciej A	given family Hanxue Gu given family Hongyu He given family Roy Colglazier given family Jordan Axelrod given family Robert French given family Maciej A Mazurowski	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/gu24b/gu24b.pdf