Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

RESPAN: an accurate, unbiased and automated pipeline for analysis of dendritic morphology and dendritic spine mapping

Accurate and unbiased reconstructions of neuronal morphology, including quantification of dendritic spine morphology and distribution, are widely used in neuroscience but remain a major roadblock for large-scale analysis. Traditionally, spine analysis has required labor-intensive manual annotation, which is prone to human error and impractical for large 3D datasets. Previous automated tools for reconstructing neuronal morphology and quantitative dendritic spine analysis face challenges in generating accurate results and, following close inspection, often require extensive manual correction. While recent tools leveraging deep learning approaches have substantially increased accuracy, they lack functionality and useful outputs, necessitating additional tools to perform a complete analysis and limiting their utility. In this paper, we describe Restoration Enhanced SPine And Neuron (RESPAN) analysis, a new comprehensive pipeline developed as an open-source, easily deployable solution that harnesses recent advances in deep learning and GPU processing. Our approach demonstrates high accuracy and robustness, validated extensively across a range of imaging modalities for automated dendrite and spine mapping. It also offers extensive visual and tabulated data outputs, including detailed morphological and spatial metrics, dendritic spine classification, and 3D renderings. Additionally, RESPAN includes tools for validating results, ensuring scientific rigor and reproducibility.

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

Efficient high-resolution fluorescence projection imaging over an extended depth of field through optical hardware and deep learning optimizations

Optical microscopy has witnessed notable advancements but has also become more costly and complex. Conventional wide field microscopy (WFM) has low resolution and shallow depth-of-field (DOF), which limits its applications in practical biological experiments. Recently, confocal and light sheet microscopy become major workhorses for biology that incorporate high-precision scanning to perform imaging within an extended DOF but at the sacrifice of expense, complexity, and imaging speed. Here, we propose deep focus microscopy, an efficient framework optimized both in hardware and algorithm to address the tradeoff between resolution and DOF. Our deep focus microscopy achieves large-DOF and high-resolution projection imaging by integrating a deep focus network (DFnet) into light field microscopy (LFM) setups. Based on our constructed dataset, deep focus microscopy features a significantly enhanced spatial resolution of ∼260 nm, an extended DOF of over 30 µm, and broad generalization across diverse sample structures. It also reduces the computational costs by four orders of magnitude compared to conventional LFM technologies. We demonstrate the excellent performance of deep focus microscopy in vivo, including long-term observations of cell division and migrasome formation in zebrafish embryos and mouse livers at high resolution without background contamination.

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

Blueprints from plane to space: outlook of next-generation three-dimensional histopathology

Here, we summarize the literature relevant to recent advances in three-dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light-sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross-disciplinary collaboration and innovation.

Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning

Scanning electron microscopy (SEM) is a crucial tool for analyzing submicron-scale structures. However, the attainment of high-quality SEM images is contingent upon the high conductivity of the material due to constraints imposed by its imaging principles. For weakly conductive materials or structures induced by intrinsic properties or organic doping, the SEM imaging quality is significantly compromised, thereby impeding the accuracy of subsequent structure-related analyses. Moreover, the unavailability of paired high-low quality images in this context renders the supervised-based image processing methods ineffective in addressing this challenge. Here, an unsupervised method based on Cycle-consistent Generative Adversarial Network (CycleGAN) was proposed to enhance the quality of SEM images for weakly conductive samples. The unsupervised model can perform end-to-end learning using unpaired blurred and clear SEM images from weakly and well-conductive samples, respectively. To address the requirements of material structure analysis, an edge loss function was further introduced to recover finer details in the network-generated images. Various quantitative evaluations substantiate the efficacy of the proposed method in SEM image quality improvement with better performance than the traditional methods. Our framework broadens the application of artificial intelligence in materials analysis, holding significant implications in fields such as materials science and image restoration.

Photon-level single-pixel 3D tomography with masked attention network

Tomography plays an important role in characterizing the three-dimensional structure of samples within specialized scenarios. In the paper, a masked attention network is presented to eliminate interference from different layers of the sample, substantially enhancing the resolution for photon-level single-pixel tomographic imaging. The simulation and experimental results have demonstrated that the axial resolution and lateral resolution of the imaging system can be improved by about 3 and 2 times respectively, with a sampling rate of 3.0 %. The scheme is expected to be seamlessly integrated into various tomography systems, which is conducive to promoting the tomographic imaging for biology, medicine, and materials science.

Registration-free 3D super-resolution generative deep-learning network for fluorescence microscopy imaging

Volumetric fluorescence microscopy has a great demand for high-resolution (HR) imaging and comes at the cost of sophisticated imaging solutions. Image super-resolution (SR) methods offer an effective way to recover HR images from low-resolution (LR) images. Nevertheless, these methods require pixel-level registered LR and HR images, posing a challenge in accurate image registration. To address these issues, we propose a novel registration-free image SR method. Our method conducts SR training and prediction directly on unregistered LR and HR volume neuronal images. The network is built on the CycleGAN framework and the 3D UNet based on attention mechanism. We evaluated our method on LR (5×/0.16-NA) and HR (20×/1.0-NA) fluorescence volume neuronal images collected by light-sheet microscopy. Compared to other super-resolution methods, our approach achieved the best reconstruction results. Our method shows promise for wide applications in the field of neuronal image super-resolution.