PyJAMAS: open-source, multimodal segmentation and analysis of microscopy images

Fernández-González, Rodrigo; Balaghi, Negar; Wang, Kelly; Hawkins, Ray; Rothenberg, Katheryn E.; McFaul, Christopher; Schimmer, Clara; Ly, Michelle; Carmo, Ana Maria do; Scepanovic, Gordana; Erdemci-Tandogan, Gonca; Castle, Veronica

doi:10.1093/bioinformatics/btab589

Cited by 18 publications

(13 citation statements)

References 18 publications

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“…1a, Movie S1). We used a support vector machine—a supervised machine learning classifier (Wang and Fernandez-Gonzalez, 2017; Fernandez-Gonzalez et al, 2021)—to detect cardioblast nuclei, and watershed-based segmentation coupled with particle image velocimetry (Wang et al, 2017) to delineate and track individual nuclei and reconstruct their trajectories (Figs. 1a’-a’’ and S1a-b, Movie S2).…”

Section: Resultsmentioning

confidence: 99%

“…Image analysis was performed using our open-source image analysis platforms, PyJAMAS (Fernandez-Gonzalez et al, 2021) and SIESTA (Fernandez-Gonzalez and Zallen, 2011). To automatically detect the position of cardioblast nuclei from microscopy movies, we used a support vector machine, a supervised machine-learning algorithm (Wang and Fernandez-Gonzalez, 2017), to distinguish cell nuclei from the background.…”

Section: Methodsmentioning

confidence: 99%

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Myosin waves and a mechanical asymmetry guide the oscillatory migration of Drosophila cardiac progenitors

Balaghi

Erdemci-Tandogan

McFaul

et al. 2022

Preprint

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Heart development begins with the formation of a tube, as cardiac progenitors migrate from opposite sides of the embryo and meet medially. Defective cardiac progenitor movements cause congenital heart defects. However, the mechanisms of cell migration during early heart development remain poorly understood. We investigated the mechanisms of movement of the Drosophila cardiac progenitors, the cardioblasts. Using quantitative time-lapse microscopy, we found that cardioblasts did not advance monotonically. Instead, cardioblasts took periodic forward and backward steps as they migrated. The forward steps were greater in both amplitude and duration, resulting in net forward movement of the cells. The molecular motor non-muscle myosin II displayed an alternating pattern of localization between the leading and trailing ends of migrating cardioblasts, forming oscillatory waves that traversed the cells. The alternating pattern of myosin polarity was associated with the alternative contraction and relaxation of the leading and trailing edges of the cell. Mathematical modelling predicted that forward migration requires the presence of a stiff boundary at the trailing edge of the cardioblasts. Consistent with this, we found a supracellular actin cable at the trailing edge of the cardioblasts. When we reduced the tension sustained by the actin cable, the amplitude of the backward steps of cardioblasts increased, thus reducing the net forward speed of migration. Together, our results indicate that periodic cell shape changes coupled with a polarized actin cable produce asymmetrical forces that guide cardioblast migration.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Myosin waves and a mechanical asymmetry guide the oscillatory migration of Drosophila cardiac progenitors

Balaghi

Erdemci-Tandogan

McFaul

et al. 2022

Preprint

Self Cite

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“…The notable aspect of this research study was the Cascade Mask-RCNN neural network that was used to guide evaluation. Fernandez-Gonzalez et al, (2022) published recent study to key in on open source programming for image processing and evaluation written in Python. The software program produces numerous segmentation equipment, along with watershed and Artificial Intelligence learning-primarily based totally methods; besides, it takes benefit of Jupyter notebooks for the show and reproducibility of facts analyses; and may be used via a cross-platform graphical person interface or as a part of Python scripts through a complete utility programming interface.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

Owoeye¹,

Ojo-Omoniyi²

2022

Int.J.Curr.Microbiol.App.Sci

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Digital image processing involves the usage of a functional algorithm to process images with special regions of interest. In most case scenarios, this is termed as an active aspect of digital signal processing; image processing comes with several rewards over analog image processing. Its relevance and application spans Autonomous Vehicles, Biometric fingerprint technologies as well as Face recognition applications. Reliable statistics through feature engineering from the image can be extracted and in turn serve as focus points of deep learning insights. Besides, its application in monitoring Climatic changes, Agricultural crop yields, security measures, industrial manufacturing as well as medical fields exponentially advances each day. Meanwhile, deep learning being a feature of Artificial Intelligence has brought forward several useful models that is being used as transfer base for further model accuracies and baselines. In this study, we make use of a certain Microscopy datasets, sampling one of the images for digital processing, in order to gain useful insights through Cropped Quantizing, Laplace Edge Detection and Gaussian noise with sigma methods respectively. The statistical results of the extracted image features through Support Vector Method (SVM) give accuracy of up to 75%.

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“…The development of a user-friendly graphical user interface (GUI) therefore appears necessary to facilitate the selection of parameters, the analysis and the visualization of 3D þ time trajectories estimated from complex 3D videos. The use of Python, a versatile and free programming language is growing rapidly within the bioimaging user community (Fernandez-Gonzalez et al, 2022). Python tools for visualization [e.g.…”

Section: Introductionmentioning

confidence: 99%

STracking: a free and open-source Python library for particle tracking and analysis

et al. 2022

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Summary Analysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking…) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a python library that allows combining algorithms into standardized particle tracking pipelines. Availability and Implementation STracking is available as a python library using “pip install” and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using “pip install”. The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). Supplementary information Supplementary data are available at Bioinformatics online.

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PyJAMAS: open-source, multimodal segmentation and analysis of microscopy images

Cited by 18 publications

References 18 publications

Myosin waves and a mechanical asymmetry guide the oscillatory migration of Drosophila cardiac progenitors

Myosin waves and a mechanical asymmetry guide the oscillatory migration of Drosophila cardiac progenitors

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

STracking: a free and open-source Python library for particle tracking and analysis

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