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A Scanning Electron Microscopy-Compatible Optical Imaging Method for Mesoscopic All-Cell Brain Mapping

Understanding the structural and functional relationships within complex neural networks in the brain requires the construction of brain atlases that ...

Hefei Comprehensive National Science Center

Fudan University

Glioblastoma stem cells (GSCs) and their interplay with tumor-associated macrophages (TAMs) are responsible for malignant growth and tumor recurrence of glioblastoma multiforme (GBM), but the underlying mechanisms are largely unknown.

USF1/CD90 signaling in maintaining glioblastoma stem cells and tumor-associated macrophages adhesion.

Serial scanning electron microscopy (sSEM) has recently been developed to reconstruct complex largescale neural connectomes, through learning-based instance segmentation. However, blurry images are inevitable amid prolonged automated data acquisition due to imprecision in autofocusing and autostigmation, which impose a great challenge to accurate segmentation of the massive sSEM image data. Recently, learning-based methods, such as adversarial learning and supervised learning, have been proven to be effective for blind EM image deblurring. However, in practice, these methods suffer from the limited training dataset and the underrepresentation of high-resolution decoded features. Here, we propose a semisupervised learning guided progressive decoding network (SGPN) to exploit unlabeled blurry images for training and progressively enrich high-resolution feature representation. The proposed method outperforms the latest deblurring models on real SEM images with much less ground truth input. The improvement of the PSNR and SSIM is 1.04 dB and 0.086, respectively. We then trained segmentation models with deblurred datasets and demonstrated significant improvement in segmentation accuracy. The A-rand (Bogovic et al. 2013) decreased by 0.119 and 0.026, respectively, for 2D and 3D segmentation.

Improving the Neural Segmentation of Blurry Serial SEM Images by Blind Deblurring.

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.

A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping.

The exorbitant cost of accurately annotating the large-scale serial scanning electron microscope (SEM) images as the ground truth for training has always been a great challenge for brain map reconstruction by deep learning methods in neural connectome studies. The representation ability of the model is strongly correlated with the number of such high-quality labels. Recently, the masked autoencoder (MAE) has been shown to effectively pre-train Vision Transformers (ViT) to improve their representational capabilities.

Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder.

[This corrects the article DOI: 10.3389/fninf.2023.1118419.].

Corrigendum: Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder.

Large-scale electron microscopy (EM) has enabled the reconstruction of brain connectomes at the synaptic level by serially scanning over massive areas of sample sections. The acquired big EM data sets raise the great challenge of image mosaicking at high accuracy. Currently, it simply follows the conventional algorithms designed for natural images, which are usually composed of only a few tiles, using a single type of keypoint feature that would sacrifice speed for stronger performance. Even so, in the process of stitching hundreds of thousands of tiles for large EM data, errors are still inevitable and diverse. Moreover, there has not yet been an appropriate metric to quantitatively evaluate the stitching of biomedical EM images. Here we propose a two-stage error detection method to improve the EM image mosaicking. It firstly uses point-based error detection in combination with a hybrid feature framework to expedite the stitching computation while maintaining high accuracy. Following is the second detection of unresolved errors with a newly designed metric of EM stitched image quality assessment (EMSIQA). The novel detection-based mosaicking pipeline is tested on large EM data sets and proven to be more effective and as accurate when compared with existing methods.

Two-stage error detection to improve electron microscopy image mosaicking.

Autism spectrum disorder (ASD) encompasses a diverse range of neurodevelopmental disorders with complex etiologies, including genetic, environmental, and neuroanatomical factors. While the exact mechanisms underlying ASD remain unclear, structural abnormalities in the brain offer valuable insights into its pathophysiology. The corpus callosum, the largest white matter tract in the brain, plays a crucial role in interhemispheric communication, and its structural abnormalities may contribute to ASD-related phenotypes.

Article	Year
USF1/CD90 signaling in maintaining glioblastoma stem cells and tumor-associated macrophages adhesion. Neuro-oncology\| PubMed ID: 35287174	2022
Improving the Neural Segmentation of Blurry Serial SEM Images by Blind Deblurring. Computational intelligence and neuroscience\| PubMed ID: 36711194	2023
A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping. Brain sciences\| PubMed ID: 37239183	2023
Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder. Frontiers in neuroinformatics\| PubMed ID: 37360945	2023
Corrigendum: Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder. Frontiers in neuroinformatics\| PubMed ID: 38088986	2023
Two-stage error detection to improve electron microscopy image mosaicking. Computers in biology and medicine\| PubMed ID: 38909449	2024
Large-scale EM data reveals myelinated axonal changes and altered connectivity in the corpus callosum of an autism mouse model. Frontiers in neuroinformatics\| PubMed ID: 40290520	2025

Ruobing Zhang

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