TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning.
TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning.
复制标题
DOI：
10.1016/j.patter.2021.100367
复制
发表时间：
2021-11-12
期刊：
影响因子：
--
通讯作者：
Vitriol EA
中科院分区：
文献类型：
--
作者：
Edwards P;Skruber K;Milićević N;Heidings JB;Read TA;Bubenik P;Vitriol EA
机器学习的最新进展大大增强了从荧光显微镜数据中提取信息的自动方法。然而，目前基于机器学习的模型可能需要数百到数千张图像来训练，并且最容易访问的模型对图像进行分类，而不描述图像的哪些部分有助于分类。在这里，我们介绍TDAExplore，一个基于拓扑数据分析的机器学习图像分析管道。它可以在仅使用20 - 30张高分辨率图像进行训练后对不同类型的细胞扰动进行分类，并在来自多个受试者和显微镜模式的图像上表现出色。TDAExplore仅使用图像和整个图像标签进行训练，提供定量的空间信息，表征哪些图像区域有助于分类。训练TDAExplore模型的计算要求是适度的，标准PC可以以最少的用户输入执行训练。因此，TDAExplore是一个可访问的，功能强大的选项，用于在各种应用中获取有关成像数据的定量信息。TDAExplore将拓扑数据分析与机器学习分类相结合只有20 - 30张高分辨率图像可用于训练TDAExplore模型TDAExplore对不同的显微镜模式、数据集大小、图像特征具有鲁棒性TDAExplore量化每张图像与训练数据的相似之处和相似程度传统的荧光显微镜数据基于强度的测量限制了其揭示样本新信息的潜力。在这里，我们提出了一个名为TDAExplore的图像分析管道，它基于拓扑数据分析和机器学习分类。除了在将图像分配到正确的组中时高度准确外，TDAExplore还量化了图像与训练数据的相似程度，并确定了哪些部分不同，这是对其他机器学习模型的改进，这些模型无法深入了解分类任务是如何完成的。TDAExplore的下一步将是将其功能扩展到三维，多变量和时间序列数据集。这项工作代表了未来的进步，机器学习可以识别和描述细微的图像特征，使研究人员能够回答重要的生物学问题，并为未来的研究产生新的假设。机器学习在将成像数据分类为不同类别方面非常强大，但大多数方法都无法深入了解分类任务是如何完成的。在这里，Edwards et al. TDAExplore是一种结合机器学习分类与拓扑数据分析的图像分析管道，可解决这一限制。除了能够准确地对各种示例中的荧光显微镜数据进行分类外，TDAExplore还可以量化图像在何处以及在多大程度上与用于训练它的数据相似。
Recent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20–30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image regions contribute to classification. Computational requirements to train TDAExplore models are modest and a standard PC can perform training with minimal user input. TDAExplore is therefore an accessible, powerful option for obtaining quantitative information about imaging data in a wide variety of applications. TDAExplore combines topological data analysis with machine learning classification As few as 20–30 high-resolution images can be used to train TDAExplore models TDAExplore is robust to different microscopy modes, dataset size, image features TDAExplore quantifies where and how much each image resembles the training data Traditional intensity-based measurements of fluorescent microscopy data limit its potential to reveal new information about its sample. Here, we present an image analysis pipeline called TDAExplore, which is based on topological data analysis and machine learning classification. In addition to being highly accurate in assigning images to their correct group, TDAExplore quantifies how much images resemble the training data and identifies which parts are different, an improvement over other machine learning models that do not permit insight into how classification tasks were made. The next steps for TDAExplore will be to expand its capabilities into three-dimensional, multivariate, and time series datasets. This work represents progress into a future where machine learning identifies and describes nuanced image features in ways that allow researchers to answer important biological questions and generate new hypotheses for future studies. Machine learning is exceptionally powerful at categorizing imaging data into different classes, yet most methods do not provide insight into how classification tasks were made. Here, Edwards et al. address this limitation with TDAExplore, an image analysis pipeline combining machine learning classification with topological data analysis. In addition to being able to accurately classify fluorescent microscopy data over a broad range of examples, TDAExplore quantifies where and how much images resemble the data that were used to train it.
登录
查看更多内容
影响因子：
4
作者：
Barnes D;Polanco L;Perea JA
通讯作者：
Perea JA
影响因子：
9.8
作者：
McQuin C;Goodman A;Chernyshev V;Kamentsky L;Cimini BA;Karhohs KW;Doan M;Ding L;Rafelski SM;Thirstrup D;Wiegraebe W;Singh S;Becker T;Caicedo JC;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
48
作者：
Caicedo JC;Cooper S;Heigwer F;Warchal S;Qiu P;Molnar C;Vasilevich AS;Barry JD;Bansal HS;Kraus O;Wawer M;Paavolainen L;Herrmann MD;Rohban M;Hung J;Hennig H;Concannon J;Smith I;Clemons PA;Singh S;Rees P;Horvath P;Linington RG;Carpenter AE
通讯作者：
Carpenter AE
DOI：
10.1006/ciun.1993.1024
发表时间：
1993-05-01
期刊：
CVGIP-IMAGE UNDERSTANDING
影响因子：
--
作者：
REED, TR;DUBUF, JMH
通讯作者：
DUBUF, JMH