基于深度学习的多分辨显微关联成像系统设计

System design of multi-resolution microscopic correlation imaging based on deep learning

显微成像技术作为研究细胞和生物组织的主要工具,对生物医学的发展起到了极大的推动作用。生物样本的复杂化和生物医学领域对时间和空间分辨率的多样化需求决定了单一功能生物成像系统应用的局限性。为满足生物医学领域的多样化需求,解决成像质量与成像时间之间的矛盾,设计了一种基于深度学习的多分辨显微关联成像系统。该系统通过对显微镜进行硬件设计改造和软件处理,将深度学习与关联成像技术有效结合,当采样率仅为60%时,成像系统能够较好地恢复图像细节,大幅降低欠采样带来的噪声,同时显著提升系统成像的时间分辨率。另外,为了满足所设计的小型多分辨显微关联成像系统的实际需求,采用基于重参数化思想的超高效轻量超分网络,在资源受限的设备下实现实时高质量成像。所提出的成像系统可以在保证成像质量的同时显著缩短成像时间和减少内存占用。不同类型生物样本和分辨率板的测试结果进一步表明了系统的鲁棒性和抗噪性能,研究结果对生物医学领域具有重要意义。

Objective Microscopic imaging technology is the primary research method for biological organs,tissues and cells.It plays a significant role in promoting the development of biology and medicine.However,the diversity and complexity of biological samples,the low signal-to-noise ratio,and the optical diffraction limit of traditional optical microscopy significantly limit its application.Different biological samples and different application scenarios have different requirements for microscopic imaging technology.Therefore,in clinical applications,how to obtain images with appropriate resolution through practical needs,and how to shorten imaging time while ensuring imaging quality are the problems that need to be solved urgently in microscopic imaging applications.Methods The microscope is modified by adding a beam-splitting device in the optical path.The light that carries the sample information was exported to the multi-resolution microscopic correlation imaging system after being magnified by the objective lens.The experimental system was integrated into the shell(24 cm×18 cm×12 cm,Fig.2).The optical signal is illuminated to DMD,and the signal light is modulated by DMD and received by a single-pixel detector.The reconstructed images of the sample are obtained through the second-order correlation operation of the modulation matrix and detection intensity of a single-pixel detector.The imaging system was equipped with an industrial computer and a data acquisition card,which are used to control the DMD,load the preset pattern and record the light intensity collected by the detector.The reconstructed images of the sample are obtained through the second-order correlation operation of the modulation matrix and detection intensity of a single-pixel detector.Then the images are processed through deep learning.Results and Discussions The tissue slice was used as the target object,and the performance of the DLGI system after hardware and software design were tested.The imaging results under five different sampling rates were obtained(image resolution:128×128,Fig.5 and Fig.6).With the decrease of the sampling rate,the imaging quality is reduced significantly,accompanied by a large amount of noise.When the sampling rate reaches 60%,the internal details of biological tissue in traditional correlation imaging(GI)cannot be recognized,and it is unacceptable for pathological section observation.The image quality is significantly improved after using the deep learning method.Even when the sampling rate is 60%,the internal details and edge contours of biological tissues can be restored clearly,and the image noise is significantly improved.In this paper,the ultra-efficient and lightweight hyper-division network based on heavy parameterization reduced the complexity of image calculation significantly(Fig.3),and the reasoning time can reach 51 ms.The imaging time of the imaging system in this paper can save 0.37 s while ensuring the imaging quality and significantly reducing the memory occupation(Tab.1).Conclusions A multi-resolution microscopic correlated imaging based on deep learning is designed to meet the diverse needs of microscopic imaging and solve the contradiction between imaging quality and imaging time in practical application.The system combines deep learning with correlation imaging technology through hardware design and software processing of the microscope.The imaging system can restore image details with a sampling rate of only 60%,significantly reduce the noise caused by under-sampling,and significantly improve the time resolution of the system.In addition,to meet the actual needs of the small-scale imaging systems,an ultraefficient super-resolution network is adopted based on the overparameterization method to realize real-time imaging under equipment with limited resources.The proposed imaging system can significantly reduce the imaging time and memory occupation while maintaining imaging quality.The test results of different types of biological samples and resolution boards further show the robustness and anti-noise performance of the system.The research results of the system have great significance for the biomedical field.