Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Periodical |
| Magazine / Source | Scientific Reports |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1038/s41598-019-49431-3 |
| Doi | http://dx.doi.org/10.1038/s41598-019-49431-3 |
| Keywords | image segmentation;deep learning;smal extracellular vesicles;transmission electron microscopy |
| Description | Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, refecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantifcation of sEVs an extremely difcult task. We present a completely deep-learningbased pipeline for the segmentation of seVs in teM images. our method applies a residual convolutional neural network to obtain fne masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two diferent state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications. |
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