A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent...

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Bibliographic Details
Published in:NMR in biomedicine 2024-08, Vol.37 (8), p.e5143-n/a
Main Authors: Liu, Zelong, Kainth, Komal, Zhou, Alexander, Deyer, Timothy W., Fayad, Zahi A., Greenspan, Hayit, Mei, Xueyan
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Brain - diagnostic imaging
Deep Learning
few‐shot learning
GAN
Health services
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Medical technology
Medical treatment
MRI segmentation
Neuroimaging
Reviews
self‐supervised learning
semi‐supervised learning
Supervised learning
Supervised Machine Learning
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