CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy.
CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy.
复制标题
DOI：
10.1038/s42003-021-01721-1
复制
发表时间：
2021-02-15
影响因子：
5.9
通讯作者：
Philip NS
中科院分区：
文献类型：
--
作者：
George B;Assaiya A;Roy RJ;Kembhavi A;Chauhan R;Paul G;Kumar J;Philip NS
粒子鉴定和选择，这是通过单粒子冷冻电子显微镜对生物大分子进行高分辨率结构测定的先决条件，这是自动化结构确定步骤的主要瓶颈。在这里，我们提出了一种广义的深度学习工具，即Cassper，用于在透射显微镜图像中自动检测和隔离蛋白质颗粒。该深度学习工具使用语义分割和视觉上准备的训练样品的集合来捕获显微照片中蛋白质，冰，碳和其他杂质的传播强度的差异。 Cassper是一种基于语义分割的方法，可进行像素级分类，并完全消除对手动粒子拾取的需求。对比度的对比度有限的自适应直方图均衡（CLAHE）在卡斯珀中可以使冰层厚度和对比度可变的显微照片中的高保真粒子检测。广义的卡斯珀模型在看不见的数据集上具有很高的效率，并且可以在直觉上选择粒子，从而使数据处理自动化。
乔治，Assaiya等。开发一种深度学习工具卡斯珀，该工具可自动化传输显微镜图像中蛋白质颗粒的检测。该算法使用语义分割和视觉上准备的训练样品来捕获显微镜图像的传输强度差异，从而实现数据处理的自动化。
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. George, Assaiya et al. develop a deep learning tool, CASSPER, that automates the detection of protein particles in transmission microscope images. This algorithm uses semantic segmentation and visually prepared training samples to capture the differences in the transmission intensities of microscope images, enabling automation of data processing.
登录
查看更多内容
影响因子：
3
作者：
Tang, Guang;Peng, Liwei;Ludtke, Steven J.
通讯作者：
Ludtke, Steven J.
影响因子：
3
作者：
Chen, James Z.;Grigorieff, Nikolaus
通讯作者：
Grigorieff, Nikolaus
影响因子：
3
作者：
Heimowitz A;Andén J;Singer A
通讯作者：
Singer A
影响因子：
48
作者：
Punjani, Ali;Rubinstein, John L.;Brubaker, Marcus A.
通讯作者：
Brubaker, Marcus A.
影响因子：
2.2
作者：
Chen, Shaoxia;McMullan, Greg;Faruqi, Abdul R.;Murshudov, Garib N.;Short, Judith M.;Scheres, Sjors H. W.;Henderson, Richard
通讯作者：
Henderson, Richard