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

Eichenberger, Bastian Th; Zhan, Yinxiu; Rempfler, Markus; Giorgetti, Luca; Chao, Jeffrey A.

doi:10.1101/2020.12.14.422631

Cited by 7 publications

(18 citation statements)

References 22 publications

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“…To complete the comparison, we applied our M hybrid model to the smFISH test dataset DS dB made available with the deepBlink publication and containing 129 smFISH images. The results reported for the prediction of M dB for the DS dB dataset is the result obtained by the authors and reported in (Eichenberger et al, 2021). Not surprisingly, deepBlink model performance is better on their own smFISH images, however DeepSpot managed to have an F1-score of nearly 88%, a noticeable achievement since the model has not been trained on the DS dB data.…”

Section: Deepspot Enables More Accurate Spot Detection Compared With Deep-blinksupporting

confidence: 61%

“…Deep-learning networks have been introduced for mRNA spot detection (Gudla et al, 2017; Mabaso et al, 2018), and more recently deepBlink (Eichenberger et al, 2021). The latter focuses on a fully convolutional neural network based on the U-Net architecture.…”

Section: Related Workmentioning

confidence: 99%

“…Other than being time-consuming, the quality of detection largely depends on the capacity of the user to correctly choose the method’s parameters according to each image properties (contrast, spots, artifacts, noise). Some recent deep-learning based approaches for mRNA spot detection try to circumvent this limitation, such as deepBlink (Eichenberger et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

Bouilhol

Lefevre

Dartigues

et al. 2021

Preprint

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Detection of RNA spots in single molecule FISH microscopy images remains a difficult task especially when applied to large volumes of data. The small size of RNA spots combined with high noise level of images often requires a manual adaptation of the spot detection thresholds for each image. In this work we introduce DeepSpot, a Deep Learning based tool specifically designed to enhance RNA spots which enables spot detection without need to resort to image per image parameter tuning. We show how our method can enable the downstream accurate detection of spots. The architecture of DeepSpot is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for the Context Aggregation for Small Object (CASO) and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced by our method, by training DeepSpot on 20 simulated and 1 experimental datasets, and have shown that more than 97% accuracy is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot allows more precise mRNA detection. In addition, we generated smFISH images from mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

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Section: Deepspot Enables More Accurate Spot Detection Compared With Deep-blinksupporting

confidence: 61%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

Bouilhol

Lefevre

Dartigues

et al. 2021

Preprint

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“…RS-FISH performance was benchmarked against the leading tools for single-molecule spot detection in images. The tools compared in the benchmarking are FISH-quant 14 (Matlab), Big-FISH 26 (Python), AIRLOCALIZE 17 (Matlab), Starfish 16 (Python), and deepBlink 15 (Python, TensorFlow). Localization performance comparison was done on simulated images with known ground truth spot locations, and computation time comparison was performed using real three-dimensional C. elegans smFISH images.…”

Section: Methodsmentioning

confidence: 99%

“…To validate and benchmark RS-FISH, we performed quantitative comparisons against FISH-quant 14 , Big-FISH 26 , AIRLOCALIZE 17 , Starfish 16 , and deepBlink 15 using (i) simulated smFISH images with varying noise levels to assess detection performance, (ii) real C. elegans embryo datasets for runtime measurements, and (iii) large lightsheet datasets 13 . We show that RS-FISH is on par with the best methods in terms of detection performance ( Fig.…”

Section: Mainmentioning

confidence: 99%

RS-FISH: Precise, interactive, fast, and scalable FISH spot detection

Bahry

Breimann

Epstein

et al. 2021

Preprint

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Summary: Studying transcription using single-molecule RNA-FISH (smFISH) is a powerful method to gain insights into gene regulation on a single cell basis, which relies on accurate identification of sub-resolution fluorescent spots in microscopy images. Here we present Radial Symmetry-FISH (RS-FISH), which can robustly and quickly detect even close smFISH spots in two and three dimensions with high precision, allows interactive parameter tuning, and can easily be applied to large sets of images. Availability and implementation: RS-FISH is an open-source implementation written in Java/ImgLib2 and provided as a macro-scriptable Fiji plugin. Source code, tutorial, documentation, and example images are available at: https://github.com/PreibischLab/RadialSymmetryLocalization.

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DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images

et al. 2022

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Detection of RNA spots in single-molecule fluorescence in-situ hybridization microscopy images remains a difficult task, especially when applied to large volumes of data. The variable intensity of RNA spots combined with the high noise level of the images often requires manual adjustment of the spot detection thresholds for each image. In this work, we introduce DeepSpot, a Deep Learning-based tool specifically designed for RNA spot enhancement that enables spot detection without the need to resort to image per image parameter tuning. We show how our method can enable downstream accurate spot detection. DeepSpot’s architecture is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for context aggregation for small object and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity, and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced with our method by testing DeepSpot on 20 simulated and 3 experimental datasets, and showed that accuracy of more than 97% is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot provides more precise mRNA detection. In addition, we generated single-molecule fluorescence in-situ hybridization images of mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

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

Cited by 7 publications

References 22 publications

DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

DeepSpot: a deep neural network for RNA spot enhancement in smFISH microscopy images

RS-FISH: Precise, interactive, fast, and scalable FISH spot detection

DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images

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