LDV Soft Inc, Aurora L4G6L6, Canada.
Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010, USA.
Microsc Microanal. 2024 Nov 4;30(5):889-902. doi: 10.1093/mam/ozae098.
Volume electron microscopy (VEM) is an essential tool for studying biological structures. Due to the challenges of sample preparation and continuous volumetric imaging, image artifacts are almost inevitable. Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used contrast limited adaptive histogram equalization (CLAHE) can alter the essential relative contrast information about some biological structures. We developed an image-processing pipeline to remove the artifacts and enhance the images without CLAHE. We apply our method to VEM datasets of a Microwasp head. We demonstrate that our method restores the images with high fidelity while preserving the original relative contrast. This pipeline is adaptable to other VEM datasets.
体式电子显微镜(VEM)是研究生物结构的重要工具。由于样本制备和连续体积成像的挑战,图像伪影几乎是不可避免的。这些图像伪影使得自动化计算机视觉方法和人类专家的进一步处理变得复杂。不幸的是,广泛使用的对比度限制自适应直方图均衡化(CLAHE)可能会改变一些生物结构的基本相对对比度信息。我们开发了一种图像处理管道,用于在不使用 CLAHE 的情况下去除伪影并增强图像。我们将我们的方法应用于 Microwasp 头部的 VEM 数据集。我们证明,我们的方法在保持原始相对对比度的同时,以高保真度恢复图像。该管道可适应其他 VEM 数据集。