Denoising Across Data Acquisition Modalities for Mesoscopic Scale Optical Neuroimaging
| Autor: | Anan Li, Yue Guan, Jing Yuan, Yutong Han, Gong Hui, Tianfang Zhu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Scale (ratio) Computer science Noise reduction 010501 environmental sciences 01 natural sciences Signal 03 medical and health sciences Optical imaging Data acquisition Neuroimaging denoising General Materials Science Computer vision 030304 developmental biology 0105 earth and related environmental sciences 0303 health sciences Noise measurement business.industry General Engineering Computer Science::Computer Vision and Pattern Recognition CycleGAN Artificial intelligence Noise (video) lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 mesoscopic optical imaging |
| Zdroj: | IEEE Access, Vol 9, Pp 23624-23632 (2021) |
| ISSN: | 2169-3536 |
| Popis: | The ever-evolving mesoscopic scale optical imaging systems facilitate the new discoveries in the neuroscience research. They excel at providing a complete view of the long projections of a single neuron across the whole brain. Although the preparation protocols and the optical imaging systems are rapidly evolving, there are still some noise and artifacts interfering downstream data processing and analysis due to the defects in the imaging system or during the sample preparation. A specific denoising procedure is usually developed for one optical imaging system as a post-processing measure. However, the development of the optical imaging systems usually follows in an incremental manner. It would be better to adapt the denoising model to the new optical imaging system than training a denoising model from scratch. In this paper, the proposed learning schema and practice learn a new denoising model for a new optical imaging system based on the existing denoise models and bootstrap a denoising model without the ground-truth denoising labels. We achieve this through a CycleGAN based model and the fact that the optical imaging systems usually produce images both from the signal area and the fixture area. The experiments show that our proposed procedure can provide a comparable denoising performance against other state-of-the-art denoising methods for several optical neuroimaging datasets. |
| Databáze: | OpenAIRE |
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