Image Processing for Volume Electron Microscopy

Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks

Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Targeting Functionally Characterized Synaptic Architecture Using Inherent Fiducials and 3D Correlative Microscopy

Brain circuits are highly interconnected three-dimensional structures fabricated from components ranging vastly in size; from cell bodies to individual synapses. While neuronal activity can be visualized with advanced light microscopy (LM) techniques, the resolution of electron microscopy (EM) is critical for identifying synaptic connections between neurons. Here, we combine these two techniques, affording the advantage of each and allowing for measurements to be made of the same neural features across imaging platforms. We established an EM-label-free workflow utilizing inherent structural features to correlate in vivo two-photon LM and volumetric scanning EM (SEM) in the ferret visual cortex. By optimizing the volume SEM sample preparation protocol, imaging with the OnPoint detector, and utilizing the focal charge compensation device during serial block-face imaging, we achieved sufficient resolution and signal-to-noise ratio to analyze synaptic ultrastructure for hundreds of synapses within sample volumes. Our novel workflow provides a reliable method for quantitatively characterizing synaptic ultrastructure in functionally imaged neurons, providing new insights into neuronal circuit organization.