Fast learning-based registration of sparse 3D clinical images
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are desig...
Main Authors: | Lewis, Kathleen M.(Kathleen Marie), Guttag, John V, Dalca, Adrian Vasile |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | English |
Published: |
Association for Computing Machinery (ACM)
2021
|
Online Access: | https://hdl.handle.net/1721.1/129553 |
Similar Items
-
Fast learning-based registration of sparse 3D clinical images
by: Lewis, Kathleen M.(Kathleen Marie), et al.
Published: (2022) -
Fast learning-based registration of sparse 3D clinical images
by: Lewis, Kathleen M.(Kathleen Marie)
Published: (2019) -
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
by: Dalca, Adrian Vasile, et al.
Published: (2021) -
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces
by: Dalca, Adrian Vasile, et al.
Published: (2021) -
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
by: Dalca, Adrian V., et al.
Published: (2021)