Learning A Self-Inverse Network for Bidirectional Mri Image Synthesis
| Autor: | B. Yifan Chen, D. Bogdan Georgescu, C. Kevin S. Zhou, F. Thomas S. Huang, A. Zengming Shen, E. Xuqi Liu |
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| Rok vydání: | 2020 |
| Předmět: |
Property (programming)
business.industry Computer science Generalization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inverse Pattern recognition Function (mathematics) 010501 environmental sciences 01 natural sciences Convolutional neural network 030218 nuclear medicine & medical imaging Domain (software engineering) 03 medical and health sciences 0302 clinical medicine Sensitivity (control systems) Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | ISBI |
| DOI: | 10.1109/isbi45749.2020.9098576 |
| Popis: | The one-to-one mapping is necessary for MRI image synthesis as MRI images are unique to the patient. State-of-the-art approaches for image synthesis from domain $X$ to domain $Y$ learn a convolutional neural network that meticulously maps between the domains. A different network is typically implemented to map along the opposite direction, from $Y$ to X. In this paper, we explore the possibility of only wielding one network for bi-directional image synthesis. In other words, such an autonomous learning network implements a self-inverse function. A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space. Most importantly, a self-inverse function guarantees a one-to-one mapping, a property that cannot be guaranteed by earlier approaches that are not self-inverse. The experiments on MRI T1 and T2 images show that, compared with the baseline approaches that use two separate models for the image synthesis along two directions, our self-inverse network achieves better synthesis results in terms of standard metrics. Finally, our sensitivity analysis confirms the feasibility of learning a one-to-one mapping function for MRI image synthesis. |
| Databáze: | OpenAIRE |
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