Spot Detection in Microscopy Images using Convolutional Neural Network with Sliding-Window Approach

Mabaso, Matsilele; Withey, Daniel; Twala, Bhekisipho

doi:10.5220/0006724200670074

Cited by 13 publications

(7 citation statements)

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“…Recent advances in deep learning have consolidated the convolutional neural network (CNN) as the state-of-the-art for computer vision applications ( 4 , 5 ). Previously, CNNs have been used for threshold-independent particle detection ( 6–8 ), however, to the extent of our knowledge, none of these approaches can localize particle positions with sub-pixel resolution. This prevents accurate positional measurements and thus their application in high-resolution microscopy.…”

Section: Introductionmentioning

confidence: 99%

deepBlink: threshold-independent detection and localization of diffraction-limited spots

Eichenberger

Zhan

Rempfler

et al. 2021

Nucleic Acids Research

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Detection of diffraction-limited spots in single-molecule microscopy images is traditionally performed with mathematical operators designed for idealized spots. This process requires manual tuning of parameters that is time-consuming and not always reliable. We have developed deepBlink, a neural network-based method to detect and localize spots automatically. We demonstrate that deepBlink outperforms other state-of-the-art methods across six publicly available datasets containing synthetic and experimental data.

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Section: Introductionmentioning

confidence: 99%

deepBlink: threshold-independent detection and localization of diffraction-limited spots

Eichenberger

Zhan

Rempfler

et al. 2021

Nucleic Acids Research

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show abstract

“…used for threshold-independent particle detection [6,7,8], however, without localizing particle centers with sub-pixel resolution. This prevents accurate positional measurements and thus their application in high-resolution microscopy.…”

mentioning

confidence: 99%

“…deepBlink requires a pre-trained model that can be obtained by training from scratch using custom images and coordinate labels (1)(2)(3) or downloaded directly (4). To predict on new data, deepBlink takes in raw microscopy images (5) and the aforementioned pre-trained model (6) to predict (7) spot coordinates. The output is saved as a CSV file (8) which can easily be used in further analysis workflows (9).…”

mentioning

confidence: 99%

deepBlink: Threshold-independent detection and localization of diffraction-limited spots

Eichenberger

Zhan

Rempfler

et al. 2020

Preprint

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Detection of diffraction-limited spots is traditionally performed with mathematical operators designed for idealized spots. This process requires manual tuning of parameters that is time-consuming and not always reliable. We have developed deepBlink, a neural network- based method to detect and localize spots automatically and demonstrate that deepBlink outperforms state-of-the-art methods across six publicly available datasets. deepBlink is open-sourced on PyPI and GitHub (https://github.com/BBQuercus/deepBlink) as a ready-to- use command-line interface.

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“…Several neural network-based tools are already available for the restoration of images with high noise levels (for example Weigert et al, 2018 ; Batson and Royer, 2019 ; Krull et al, 2019 ). Tools for network-based FISH spot detection have likewise started to emerge (for example Gudla et al, 2017 ; Mabaso et al, 2018 ). A common challenge with smFISH applications is the density of signal, particularly in the case of abundant transcripts.…”

Section: Analytical and Imaging-based Methods Required To Analyze Spamentioning

confidence: 99%

Pinpointing Cell Identity in Time and Space

Savulescu

Jacobs

Negishi

et al. 2020

Front. Mol. Biosci.

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Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

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Spot Detection in Microscopy Images using Convolutional Neural Network with Sliding-Window Approach

Cited by 13 publications

References 0 publications

deepBlink: threshold-independent detection and localization of diffraction-limited spots

deepBlink: threshold-independent detection and localization of diffraction-limited spots

deepBlink: Threshold-independent detection and localization of diffraction-limited spots

Pinpointing Cell Identity in Time and Space

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