Learning to automate cryo-electron microscopy data collection with Ptolemy

• URL: http://arxiv.org/abs/2112.01534v1
• Date: Wed, 1 Dec 2021 22:39:28 GMT
• Title: Learning to automate cryo-electron microscopy data collection with Ptolemy
• Authors: Paul T. Kim, Alex J. Noble, Anchi Cheng, Tristan Bepler
• Abstract summary: cryogenic electron microscopy (cryo-EM) has emerged as a primary method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. Currently, the process of collecting high-magnification cryo-EM micrographs requires human input and manual tuning of parameters. Here, we develop the first pipeline to automate low- and medium-magnification targeting with purpose-built algorithms.
• Score: 4.6453787256723365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past decade, cryogenic electron microscopy (cryo-EM) has emerged as a primary method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. In order to meet increasing demand for cryo-EM, automated methods to improve throughput and efficiency while lowering costs are needed. Currently, the process of collecting high-magnification cryo-EM micrographs, data collection, requires human input and manual tuning of parameters, as expert operators must navigate low- and medium-magnification images to find good high-magnification collection locations. Automating this is non-trivial: the images suffer from low signal-to-noise ratio and are affected by a range of experimental parameters that can differ for each collection session. Here, we use various computer vision algorithms, including mixture models, convolutional neural networks (CNNs), and U-Nets to develop the first pipeline to automate low- and medium-magnification targeting with purpose-built algorithms. Learned models in this pipeline are trained on a large internal dataset of images from real world cryo-EM data collection sessions, labeled with locations that were selected by operators. Using these models, we show that we can effectively detect and classify regions of interest (ROIs) in low- and medium-magnification images, and can generalize to unseen sessions, as well as to images captured using different microscopes from external facilities. We expect our pipeline, Ptolemy, will be both immediately useful as a tool for automation of cryo-EM data collection, and serve as a foundation for future advanced methods for efficient and automated cryo-EM microscopy.

Related papers

• Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks [0.0]
  We present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation.
  arXiv  Detail & Related papers  (2024-09-26T22:44:00Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy [2.0445155106382797]
  Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions. CNN based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection.
  arXiv  Detail & Related papers  (2023-05-12T22:49:36Z)
• Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images [68.8204255655161]
  We propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders.
  arXiv  Detail & Related papers  (2023-04-19T13:45:28Z)
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
  We present a novel concept of shared-context processing for whole slide histopathology images. AMIGO uses the celluar graph within the tissue to provide a single representation for a patient. We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
  arXiv  Detail & Related papers  (2023-03-01T23:37:45Z)
• Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images [2.5954872177280346]
  Machine learning could revolutionize materials research through autonomous data collection and processing. We employ a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator to augment simulated data with realistic spatial frequency information. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set.
  arXiv  Detail & Related papers  (2023-01-18T19:19:27Z)
• Application of the YOLOv5 Model for the Detection of Microobjects in the Marine Environment [101.18253437732933]
  The efficiency of using the YOLOV5 machine learning model for solving the problem of automatic de-tection and recognition of micro-objects in the marine environment is studied.
  arXiv  Detail & Related papers  (2022-11-28T10:58:50Z)
• Generation of microbial colonies dataset with deep learning style transfer [0.0]
  We introduce a strategy to generate a synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models. We show that the method is able to synthesize a dataset of realistic looking images that can be used to train a neural network model capable of localising, segmenting, and classifying five different microbial species.
  arXiv  Detail & Related papers  (2021-11-06T03:11:01Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening [7.213084307941148]
  We propose the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.
  arXiv  Detail & Related papers  (2020-07-10T20:11:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.