Myelin Detection in Fluorescence Microscopy Images Using Machine Learning.,Journal of Neuroscience Methods

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Myelin Detection in Fluorescence Microscopy Images Using Machine Learning.
Journal of Neuroscience Methods ( IF 2.7 ) Pub Date : 2020-09-12 , DOI: 10.1016/j.jneumeth.2020.108946
Sibel Çimen Yetiş ₁ , Abdulkerim Çapar ₂ , Dursun A Ekinci ₂ , Umut E Ayten ₁ , Bilal E Kerman ₃ , B Uğur Töreyin ₂

Affiliation

Background

The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens.

New method

In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total.

Results

Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of $98.84 \pm 0.09 %$ and $98.46 \pm 0.11 %$ were achieved by Boosted Trees and customized-CNN, respectively.

Comparison with existing methods

Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images.

Conclusions

Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening.

中文翻译：

使用机器学习检测荧光显微镜图像中的髓磷脂。

背景

由神经胶质细胞产生的髓鞘可隔离轴突，并支持神经系统的功能。髓鞘变性引起神经退行性疾病，例如多发性硬化症（MS）。没有用于促进髓鞘再生的MS疗法。药物发现经常涉及筛选数千种化合物。但是，这对于髓鞘再生药物是不可行的，因为髓磷脂的定量是一项人工劳动密集型的工作。因此，通过实现高含量的基于图像的药物筛选，用于加速髓磷脂检测的辅助软件的开发对于MS药物发现至关重要。