AI Article Synopsis
- Two-photon fluorescence microscopy allows for the imaging of deep neural structures in 3D, but struggles with lower image quality in the depth direction due to lens blur.
- To improve image quality, a new approach uses convolutional neural networks (CNNs) to restore isotropic images by merging data from three different viewpoints.
- The method employs a series of CNN models to handle complex processing efficiently and uses simulated images for self-supervised learning, resulting in significant enhancements in image clarity.
Article Abstract
Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed neural structures in the x-y image space, the image quality along the depth direction is lower because of lens blur, which often makes it difficult to identify the neural connectivity. To address this problem, we propose a novel approach for restoring the isotropic image volume by estimating and fusing the intersection regions of the images captured from three orthogonal viewpoints using convolutional neural networks (CNNs). Because convolution on 3D images is computationally complex, the proposed method takes the form of cascaded CNN models consisting of rigid transformation, dense registration, and deblurring networks for more efficient processing. In addition, to enable self-supervised learning, we trained the CNN models with simulated synthetic images by considering the distortions of the microscopic imaging process. Through extensive experiments, the proposed method achieved substantial image quality improvements.
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| http://dx.doi.org/10.1016/j.neunet.2022.04.011 | DOI Listing |
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Article Synopsis
- Two-photon fluorescence microscopy allows for the imaging of deep neural structures in 3D, but struggles with lower image quality in the depth direction due to lens blur.
- To improve image quality, a new approach uses convolutional neural networks (CNNs) to restore isotropic images by merging data from three different viewpoints.
- The method employs a series of CNN models to handle complex processing efficiently and uses simulated images for self-supervised learning, resulting in significant enhancements in image clarity.
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