标题： CRISP：一种用于冷冻电镜图像分割和处理的条件随机场框架 显示英文标题

标题： CRISP: A Framework for Cryo-EM Image Segmentation and Processing with Conditional Random Field

摘要： 在显微图像中区分信号和背景是冷冻电子显微镜（cryo-EM）的关键初始步骤，但由于信噪比低、存在污染物以及大小不一的密集颗粒，这一过程仍然非常耗时。尽管最近引入了图像分割技术以在像素级别区分颗粒，但低信噪比使得为监督模型训练生成准确标注变得复杂。此外，缺乏系统比较管道构建中不同设计选择的平台。因此，一个模块化框架对于理解该方法的优势和局限性以及推动进一步发展至关重要。为了解决这些挑战，我们提出了一种管道，可以从cryo-EM数据自动生成高质量的分割图作为地面真实标签。我们的模块化框架允许选择各种分割模型和损失函数。我们还集成了条件随机场（CRFs）与不同的求解器和特征集来细化粗略预测，从而产生细粒度的分割。这种灵活性有助于针对cryo-EM数据集进行最佳配置。当在有限的显微图像集上进行训练时，我们的方法在合成数据上的准确率、召回率、精确率、交并比（IoU）和F1分数均超过90%。此外，为了展示我们框架在下游分析中的有效性，我们表明由我们的管道提取的颗粒在真实实验数据集上产生的三维密度图分辨率高于现有粒子选择器生成的密度图，同时性能与专家手动校准的数据集相当。 显示英文摘要

摘要： Differentiating signals from the background in micrographs is a critical initial step for cryogenic electron microscopy (cryo-EM), yet it remains laborious due to low signal-to-noise ratio (SNR), the presence of contaminants and densely packed particles of varying sizes. Although image segmentation has recently been introduced to distinguish particles at the pixel level, the low SNR complicates the automated generation of accurate annotations for training supervised models. Moreover, platforms for systematically comparing different design choices in pipeline construction are lacking. Thus, a modular framework is essential to understand the advantages and limitations of this approach and drive further development. To address these challenges, we present a pipeline that automatically generates high-quality segmentation maps from cryo-EM data to serve as ground truth labels. Our modular framework enables the selection of various segmentation models and loss functions. We also integrate Conditional Random Fields (CRFs) with different solvers and feature sets to refine coarse predictions, thereby producing fine-grained segmentation. This flexibility facilitates optimal configurations tailored to cryo-EM datasets. When trained on a limited set of micrographs, our approach achieves over 90% accuracy, recall, precision, Intersection over Union (IoU), and F1-score on synthetic data. Furthermore, to demonstrate our framework's efficacy in downstream analyses, we show that the particles extracted by our pipeline produce 3D density maps with higher resolution than those generated by existing particle pickers on real experimental datasets, while achieving performance comparable to that of manually curated datasets from experts.