Automatic Detection and Segmentation of Exosomes in Transmission Electron Microscopy

Štěpka, Karel; Maška, Martin; Pálenik, Jakub Jozef; Pospíchalová, Vendula; Kotrbová, Anna Vyhlídalová; Ilkovics, Ladislav; Klemová, Dobromila; Hampl, Aleš; Bryja, Vı́tězslav; Matula, Pavel

doi:10.1007/978-3-319-46604-0_23

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…An updated version of an algorithm described previously [9] has been used. The algorithm considers the variability in EVs appearance in the input images by exploiting EV borders and their roughly oval shape as the discriminative features.…”

Section: Algorithmmentioning

confidence: 99%

TEM ExosomeAnalyzer: a computer‐assisted software tool for quantitative evaluation of extracellular vesicles in transmission electron microscopy images

Kotrbová

Štěpka

Maška

et al. 2019

J of Extracellular Vesicle

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Extracellular vesicles (EVs) function as important conveyers of information between cells and thus can be exploited as drug delivery systems or disease biomarkers. Transmission electron microscopy (TEM) remains the gold standard method for visualisation of EVs, however the analysis of individual EVs in TEM images is time-consuming if performed manually. Therefore, we present here a software tool for computer-assisted evaluation of EVs in TEM images. TEM ExosomeAnalyzer detects EVs based on their shape and edge contrast criteria and subsequently analyses their size and roundness. The software tool is compatible with common negative staining protocols and isolation methods used in the field of EV research; even with challenging TEM images (EVs both lighter and darker than the background, images containing artefacts or precipitated stain, etc.). If the fully-automatic analysis fails to produce correct results, users can promptly adjust the detected seeds of EVs as well as their boundaries manually. The performance of our tool was evaluated for three different modes with variable levels of human interaction, using two datasets with various heterogeneity. The semi-automatic mode analyses EVs with high success rate in the homogenous dataset (F1 score 0.9094, Jaccard coefficient 0.8218) as well as in the highly heterogeneous dataset containing EVs isolated from cell culture medium and patient samples (F1 score 0.7619, Jaccard coefficient 0.7553). Moreover, the extracted size distribution profiles of EVs isolated from malignant ascites of ovarian cancer patients overlap with those derived by cryo-EM and are comparable to NTA- and TRPS-derived data. In summary, TEM ExosomeAnalyzer is an easy-to-use software tool for evaluation of many types of vesicular microparticles and is available at http://cbia.fi.muni.cz/exosome-analyzer free of charge for non-commercial and research purposes. The web page contains also detailed description how to use the software tool including a video tutorial.

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Section: Algorithmmentioning

confidence: 99%

TEM ExosomeAnalyzer: a computer‐assisted software tool for quantitative evaluation of extracellular vesicles in transmission electron microscopy images

Kotrbová

Štěpka

Maška

et al. 2019

J of Extracellular Vesicle

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“…In supervised learning, the training data is labeled and the algorithm tries to optimize the model to minimize the classification error, thus supervised learning is well suited for classification problems. [280,281] Lin et al proposed a polarization-mapping strategy for single exosomes characterization that can be combined with machine learning to identify different tumor exosomes. They reported an ultra-sensitive SERS substrate by a specially engineered nanostructure made of silver nanoparticles embedded in mul-tilayer black phosphorus nanosheets (Ag/BP-NS), fabricated by a unique photo-driven chemical reduction method, as a SERS sensor.…”

Section: Artificial Intelligence In Single Evs Characterizationmentioning

confidence: 99%

From Conventional to Microfluidic: Progress in Extracellular Vesicle Separation and Individual Characterization

Chen

Lin

Cheng

et al. 2023

Adv Healthcare Materials

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Extracellular vesicles (EVs) are nanoscale membrane vesicles, which contain a wide variety of cargo such as proteins, miRNAs, and lipids. A growing body of evidence suggests that EVs are promising biomarkers for disease diagnosis and therapeutic strategies. Although the excellent clinical value, their use in personalized healthcare practice is not yet feasible due to their highly heterogeneous nature. Taking the difficulty of isolation and the small size of EVs into account, the characterization of EVs at a single‐particle level is both imperative and challenging. In a bid to address this critical point, more research has been directed into a microfluidic platform because of its inherent advantages in sensitivity, specificity, and throughput. This review discusses the biogenesis and heterogeneity of EVs and takes a broad view of state‐of‐the‐art advances in microfluidics‐based EV research, including not only EV separation, but also the single EV characterization of biophysical detection and biochemical analysis. To highlight the advantages of microfluidic techniques, conventional technologies are included for comparison. The current status of artificial intelligence (AI) for single EV characterization is then presented. Furthermore, the challenges and prospects of microfluidics and its combination with AI applications in single EV characterization are also discussed. In the foreseeable future, recent breakthroughs in microfluidic platforms are expected to pave the way for single EV analysis and improve applications for precision medicine.

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“…S1. Dataset 1 37 consisted of 20 heterogeneous images, containing 65 sEVs in total, imaged at random magnifications resulting in pixel sizes ranging from 0.26 nm to 0.63 nm. The images have a coarse and grainy background.…”

Section: Materials and Image Datamentioning

confidence: 99%

“…Regarding cell detection and segmentation in optical microscopy images, convolutional neural networks (CNNs) have produced the most accurate results 23,24,36 . To the best of our knowledge, there is only one published method devoted to the automatic segmentation of sEVs in TEM images: TEM ExosomeAnalyzer 18,37 . It applies a pipeline of classical image processing routines to obtain a labeled mask of sEVs under the assumption that they are almost perfect spherical objects.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Gómez-de-Mariscal

Maška

Kotrbová

et al. 2019

Sci Rep

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Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, reflecting 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 quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine 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 different 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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Automatic Detection and Segmentation of Exosomes in Transmission Electron Microscopy

Cited by 4 publications

References 10 publications

TEM ExosomeAnalyzer: a computer‐assisted software tool for quantitative evaluation of extracellular vesicles in transmission electron microscopy images

TEM ExosomeAnalyzer: a computer‐assisted software tool for quantitative evaluation of extracellular vesicles in transmission electron microscopy images

From Conventional to Microfluidic: Progress in Extracellular Vesicle Separation and Individual Characterization

Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

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