Learning Nanoscale Motion Patterns of Vesicles in Living Cells

Sekh, Arif Ahmed; Opstad, Ida S.; Birgisdottir, Åsa Birna; Myrmel, Truls; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K.

doi:10.1109/cvpr42600.2020.01403

Cited by 9 publications

(11 citation statements)

References 52 publications

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“…Analyzing and understanding the movement of nanoscale particles, such as vesicle transport in living cells, has been a challenging topic in the field of biomedical image analysis. From individual vesicle tracking based on point spread function (PSF) analysis to recent integrated deep-learning approaches [ 45 ], various methods have been applied to explain the vesicle movement.…”

Section: Discussionmentioning

confidence: 99%

Visualization Method for the Cell-Level Vesicle Transport Using Optical Flow and a Diverging Colormap

Lee

Kim

Higuchi

et al. 2021

Sensors

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Elucidation of cell-level transport mediated by vesicles within a living cell provides key information regarding viral infection processes and also drug delivery mechanisms. Although the single-particle tracking method has enabled clear analysis of individual vesicle trajectories, information regarding the entire cell-level intracellular transport is hardly obtainable, due to the difficulty in collecting a large dataset with current methods. In this paper, we propose a visualization method of vesicle transport using optical flow, based on geometric cell center estimation and vector analysis, for measuring the trafficking directions. As a quantitative visualization method for determining the intracellular transport status, the proposed method is expected to be universally exploited in various biomedical cell image analyses.

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Section: Discussionmentioning

confidence: 99%

Visualization Method for the Cell-Level Vesicle Transport Using Optical Flow and a Diverging Colormap

Lee

Kim

Higuchi

et al. 2021

Sensors

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“…Generally, neural networks (NNs) are more and more regularly used for computer vision and related machine learning tasks. In microscopy, a whole range of application areas for NNs has emerged [11], including denoising [12,13], digital staining [14][15][16][17], counting and labelling [18], tracking [19], image reconstruction [20][21][22], computational microscopy [23][24][25][26], virtual focusing [27,28], aberration estimation [29], and segmentation [30][31][32]. In the context of FPM, attempts have been made to perform the whole phase retrieval process with a neural network although using neural networks for the full FPM reconstruction pipeline is still an area of active research.…”

Section: Theory and Methodsmentioning

confidence: 99%

Object detection neural network improves Fourier ptychography reconstruction

et al. 2020

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High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system's complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique combines many images of the sample, recorded at differing illumination angles akin to computed tomography and uses error minimisation between the recorded images with those generated by a forward model. The more precise knowledge of those illumination angles is available for the image formation forward model, the better the result. Therefore, illumination estimation from the raw data is an important step and supports correct phase recovery and aberration correction. Here, we derive how illumination estimation can be cast as an object detection problem that permits the use of a fast convolutional neural network (CNN) for this task. We find that faster-RCNN delivers highly robust results and outperforms classical approaches by far with an up to 3-fold reduction in estimation errors. Intriguingly, we find that conventionally beneficial smoothing and filtering of raw data is counterproductive in this type of application. We present a detailed analysis of the network's performance and provide all our developed software openly.

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“…A4: Noise simulator − The noise simulation approach is taken from [3,49]. There are two main sources of noise.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Autoencoder architectures As noted in [49,52,53], microscopy and nanoscopy data poses several challenges as compared to the normal computer vision data because of absence of color, texture, and edge features. However, the small latent space of autoencoders (such as shown in block C1 of Fig.…”

Section: Training For Denoising (Block C Of Fig 2)mentioning

confidence: 99%

Artefact removal in ground truth and noise model deficient sub-cellular nanoscopy images using auto-encoder deep learning

Jadhav,

Acuña,

Agarwal

et al. 2020

Preprint

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Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical constraints, supervised dataset cannot be measured. Due to non-linear spatio-temporal mixing of data and valuable statistics of fluctuations from fluorescent molecules which compete with noise statistics, noise or artefact models in nanoscopy images cannot be explicitly learnt. Therefore, such problem poses unprecedented challenges to deep learning. Here, we propose a robust and versatile simulation-supervised training approach of deep learning auto-encoder architectures for the highly challenging nanoscopy images of sub-cellular structures inside biological samples. We show the proof of concept for one nanoscopy method and investigate the scope of generalizability across structures, noise models, and nanoscopy algorithms not included during simulation-supervised training. We also investigate a variety of loss functions and learning models and discuss the limitation of existing performance metrics for nanoscopy images. We generate valuable insights for this highly challenging and unsolved problem in nanoscopy, and set the foundation for application of deep learning problems in nanoscopy for life sciences.

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Learning Nanoscale Motion Patterns of Vesicles in Living Cells

Cited by 9 publications

References 52 publications

Visualization Method for the Cell-Level Vesicle Transport Using Optical Flow and a Diverging Colormap

Visualization Method for the Cell-Level Vesicle Transport Using Optical Flow and a Diverging Colormap

Object detection neural network improves Fourier ptychography reconstruction

Artefact removal in ground truth and noise model deficient sub-cellular nanoscopy images using auto-encoder deep learning

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