Machine learning based classification of mitochondrial morphologies from fluorescence microscopy images of Toxoplasma gondii cysts.
Machine learning based classification of mitochondrial morphologies from fluorescence microscopy images of Toxoplasma gondii cysts.
复制标题
DOI：
10.1371/journal.pone.0280746
复制
发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
Patwardhan, Abhijit R. R.
中科院分区：
文献类型：
--
作者：
Place, Brooke C. C.;Troublefield, Cortni A. A.;Murphy, Robert D. D.;Sinai, Anthony P. P.;Patwardhan, Abhijit R. R.
线粒体与能量和整体代谢密切相关，因此线粒体的形态对于推断细胞的代谢状态是非常有用的。在这项研究中，我们报告了一种使用有监督的机器学习来对线粒体形态进行自动分类的方法，以便一次从大量细胞中有效地进行分类。荧光显微镜图像的慢性包囊形式的弓形虫是用来进行这一发展。从典型组织囊内的数百种寄生虫中手动对这些形态进行分类是乏味的，而且容易出错。此外，由于形态上固有的生物异质性，人工分类可能存在变异性和缺乏重复性。我们使用图像分割来检测线粒体的形状，并使用多元Logistic回归模型中提取的特征将检测到的形状分为五个形态类别：斑点、蝌蚪、套索/甜甜圈、弧形和其他。首先使用从图像子集中检测到的形状来获得专家用户之间的共识分类，从而获得标记集。使用来自5个包囊的标记集对该模型进行训练，并在来自其他10个未用于训练的包囊的线粒体形态上测试其性能。结果表明，该模型的平均总体准确率为87%。斑点和弧形的分类具有很高的置信度(平均F分0.91和0.73)，它们构成了大多数形态(85%)。尽管目前的开发使用的是弓形虫组织包囊的显微图像，但该方法具有适应性，只需进行微小的调整，并可用于自动分类来自各种细胞的细胞器的形态。
The mitochondrion is intimately linked to energy and overall metabolism and therefore the morphology of mitochondrion can be very informative for inferring the metabolic state of cells. In this study we report an approach for automatic classification of mitochondrial morphologies using supervised machine learning to efficiently classify them from a large number of cells at a time. Fluorescence microscopy images of the chronic encysted form of parasite Toxoplasma gondii were used for this development. Manually classifying these morphologies from the hundreds of parasites within typical tissue cysts is tedious and error prone. In addition, because of inherent biological heterogeneity in morphologies, there can be variability and lack of reproducibility in manual classification. We used image segmentation to detect mitochondrial shapes and used features extracted from them in a multivariate logistic regression model to classify the detected shapes into five morphological classes: Blobs, Tadpoles, Lasso/Donuts, Arcs, and Other. The detected shapes from a subset of images were first used to obtain consensus classification among expert users to obtain a labeled set. The model was trained using the labeled set from five cysts and its performance was tested on the mitochondrial morphologies from ten other cysts that were not used in training. Results showed that the model had an average overall accuracy of 87%. There was high degree of confidence in the classification of Blobs and Arcs (average F scores 0.91 and 0.73) which constituted the majority of morphologies (85%). Although the current development used microscopy images from tissue cysts of Toxoplasma gondii, the approach is adaptable with minor adjustments and can be used to automatically classify morphologies of organelles from a variety of cells.
登录
查看更多内容
影响因子：
4.6
作者：
Fogo GM;Anzell AR;Maheras KJ;Raghunayakula S;Wider JM;Emaus KJ;Bryson TD;Bukowski MJ;Neumar RW;Przyklenk K;Sanderson TH
通讯作者：
Sanderson TH
影响因子：
2.1
作者：
Seeber, F;Ferguson, DJP;Gross, U
通讯作者：
Gross, U
影响因子：
5.2
作者：
Sinai AP;Watts EA;Dhara A;Murphy RD;Gentry MS;Patwardhan A
通讯作者：
Patwardhan A
影响因子：
1.8
作者：
Iqbal, Muhammad Shahid;El-Ashram, Saeed;Luo, Bin
通讯作者：
Luo, Bin
影响因子：
4.4
作者：
MEYER, F
通讯作者：
MEYER, F