A Review on Deep Learning in Medical Image Reconstruction

Autor: Haimiao Zhang, Bin Dong
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
0211 other engineering and technologies
FOS: Physical sciences
010103 numerical & computational mathematics
02 engineering and technology
Iterative reconstruction
Management Science and Operations Research
Machine learning
computer.software_genre
01 natural sciences
Machine Learning (cs.LG)
Robustness (computer science)
FOS: Electrical engineering
electronic engineering
information engineering
Medical imaging
Leverage (statistics)
0101 mathematics
Image restoration
021103 operations research
Mathematical model
Artificial neural network
business.industry
Deep learning
Image and Video Processing (eess.IV)
Electrical Engineering and Systems Science - Image and Video Processing
Physics - Medical Physics
Medical Physics (physics.med-ph)
Artificial intelligence
60H10
92C55
93C15
94A08
business
computer
Zdroj: Journal of the Operations Research Society of China. 8:311-340
ISSN: 2194-6698
2194-668X
DOI: 10.1007/s40305-019-00287-4
Popis: Medical imaging is crucial in modern clinics to guide the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision, have been playing a prominent role. Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed, and we shall call these models handcrafted models. Later, handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs, while part of the model is learned from the observed data. More recently, as more data and computation resources are made available, deep learning based models (or deep models) pushed data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs. Both handcrafted and data-driven modeling have their own advantages and disadvantages. One of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches. The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint. This viewpoint stimulates new designs of neural network architectures with inspiration from optimization algorithms and numerical differential equations. Given the popularity of deep modeling, there are still vast remaining challenges in the field, as well as opportunities which we shall discuss at the end of this article.
31 pages, 6 figures. Survey paper. Revise the typos
Databáze: OpenAIRE
Externí odkaz: