Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determination. Here, we present a generalized deep learning tool, CASSPER, for the automated detection and isolation of protein particles in transmission microscope images. This deep learning tool uses Semantic Segmentation and a collection of visually prepared training samples to capture the differences in the transmission intensities of protein, ice, carbon, and other impurities found in the micrograph. CASSPER is a semantic segmentation based method that does pixel-level classification and completely eliminates the need for manual particle picking. Integration of Contrast Limited Adaptive Histogram Equalization (CLAHE) in CASSPER enables high-fidelity particle detection in micrographs with variable ice thickness and contrast. A generalized CASSPER model works with high efficiency on unseen datasets and can potentially pick particles on-the-fly, enabling data processing automation.

颗粒识别和选择是通过单颗粒冷冻电子显微镜高分辨率生物大分子结构测定的先决条件，也是结构测定步骤自动化的主要瓶颈。在这里，我们提出了一种通用深度学习工具 CASSPER，用于自动检测和分离透射显微镜图像中的蛋白质颗粒。这种深度学习工具使用语义分割和一系列视觉准备的训练样本来捕获显微照片中发现的蛋白质、冰、碳和其他杂质的传输强度的差异。 CASSPER 是一种基于语义分割的方法，可以进行像素级分类，并完全消除手动粒子拾取的需要。 CASSPER 中对比度有限自适应直方图均衡 (CLAHE) 的集成可在具有可变冰厚度和对比度的显微照片中实现高保真粒子检测。广义的 CASSPER 模型可以在未见过的数据集上高效工作，并且可以动态拾取粒子，从而实现数据处理自动化。