Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, Japan.
Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Japan.
Neural Netw. 2022 Aug;152:57-69. doi: 10.1016/j.neunet.2022.04.011. Epub 2022 Apr 19.
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.
双光子荧光显微镜使对深层皮质区域的三维（3D）神经成像成为可能。虽然它可以在 x-y 图像空间中捕获详细的神经结构，但由于镜头模糊，深度方向上的图像质量较低，这通常使得难以识别神经连接。为了解决这个问题，我们提出了一种新的方法，通过使用卷积神经网络（CNN）估计和融合从三个正交视点捕获的图像的交叉区域来恢复各向同性的图像体积。由于在 3D 图像上进行卷积计算复杂，因此所提出的方法采用级联 CNN 模型的形式，该模型由刚性变换、密集配准和去模糊网络组成，以实现更高效的处理。此外，为了实现自我监督学习，我们通过考虑微观成像过程的失真，用模拟的合成图像来训练 CNN 模型。通过广泛的实验，所提出的方法实现了图像质量的显著提高。
