ImagePy: an open-source, Python-based and platform-independent software package for bioimage analysis

Wang, An‐Liang; Yan, Xiaolong; Wei, Zhijun

doi:10.1093/bioinformatics/bty313

Cited by 38 publications

(21 citation statements)

References 12 publications

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“…Although several general and mature bioimaging tools like ImageJ [6], Icy [7], CellProfiler [8] and ImagePy [9] exist, none of these provide support for all of the aforementioned steps nor do they possess an interface optimised for the workflow as a whole. A detailed comparison with other systems is provided in Section 3.1.…”

Section: Typical Steps Of Nucleus Analysis Workflowsmentioning

confidence: 99%

PartSeg, a Tool for Quantitative Feature Extraction From 3D Microscopy Images for Dummies

Bokota

Sroka

Basu

et al. 2020

Preprint

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Background: Bioimaging techniques offer a robust tool for studying molec- ular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high resolution 3D microscopy provides access to an ever-increasing amount of high quality images, there arises a need for their analysis in an automated, unbiased and simple way. Segmentation of structures within cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time the available segmentation tools provide a steep learning curve for new users with limited technical background. This is especially apparent in bulk processing of image sets, which requires the use of some form of programming notation. Results: In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. Conclusions: In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for bulk processing of multiple images and its components can be reused in other systems or computational experiments. Contact: g.bokota@cent.uw.edu.pl, a.magalska@nencki.edu.pl, d.plewczynski@cent.uw.edu.pl

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Section: Typical Steps Of Nucleus Analysis Workflowsmentioning

confidence: 99%

PartSeg, a Tool for Quantitative Feature Extraction From 3D Microscopy Images for Dummies

Bokota

Sroka

Basu

et al. 2020

Preprint

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“…The open-source software ImagePy was used to complete image processing, visualization and quantification of the three-dimensional aggregate structure (Wang et al, 2018). To avoid the influences of soil depth and image boundary, a total of 512 images with serial numbers 512-1024 were selected, and a nonedge section of 512×512×512 pixels (i.e., 1.664 mm×1.664 mm×1.664 mm) in size was selected as the target area for image analysis.…”

Section: σρ-μ῝τ σςαννινγ ανδ ιμαγε προςεσσινγmentioning

confidence: 99%

“…This study used the automatic Otsu algorithm of the global threshold method (Zhou et al, 2012;Garbout et al, 2013) to perform binary segmentation which was subsequently completed by visual observation and manual adjustment. After image binarization, the three-dimensional visualization function of the ImagePy software was used to visualize the three-dimensional structure of soil aggregates (Wang et al, 2018) (Figure 2).…”

Section: σρ-μ῝τ σςαννινγ ανδ ιμαγε προςεσσινγmentioning

confidence: 99%

“…ImagePy software (Wang et al, 2018) was used to analyze the segmented binary image and obtain the quantity, diameter, surface area and volume of soil aggregate particles; subsequently, the 3D structural characteristics of the aggregate after splashing with different raindrop sizes were obtained through calculation and analysis. Aggregate fragments were divided into six classes according to their size: 500-1000, 250-500, 106-250, 53-106, 25-53 and <25 μm.…”

Section: Calculation and Analysismentioning

confidence: 99%

“…FD was calculated by linear regression of log{N(ε)} vs log(1/ε), and the range was 2-3. Fractal dimension was computed using the open-source software ImagePy (Wang et al, 2018). Specific surface area is volumespecific surface area, expressed as the ratio of surface area to volume of each aggregate fragment.…”

Section: Calculation and Analysismentioning

confidence: 99%

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Impact of raindrop sizes and intensities on the microcharacteristics of soil aggregates

Yang

et al. 2020

Preprint

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Aggregate breakdown caused by the impact of raindrops clogs soil pores, reduces soil infiltration and aggravates the formation of soil crusts. To determine the influence of raindrop splash on the microstructure of soil aggregates, the typical loess was studied. We used synchrotron-based X-ray microcomputed tomography (SR-μCT) to analyze the microcharacteristics of soils impacted by rainfall intensities. The results showed that raindrop splash increases the number of surface soil aggregates, especially when the rainfall intensity is 68.61 or 217.26 mm h-1. Compared to the undisturbed soil, the number of soil aggregates increased by 38.71%, 46.77% and 76.77%, and the volume increased by 1.09%, 3.21% and 3.73%, after the impact of rainfall intensities of 5.76, 68.61 and 217.26 mm*h-1, respectively. Raindrop impact on the surface affects the distribution of aggregate particles, causing a decrease in the number of aggregate particles in the 500-1000 μm range and an increase in the < 500 μm range. Compared with unsplashed soil, there is a significant increase in the fractal dimension (FD) and total specific surface area (SSA) of surface soil aggregates in splashed soil. Particularly, the rainfall intensity of 217.26 mm h-1 resulted in an increase of the FD and SSA by 30.24% and 17.49%, respectively. Under the rainfall intensities, the average particle diameter of the soil aggregates decreased by 2.43%, 3.25% and 3.55%, respectively, compared with that of the undisturbed soil. These results indicated that raindrop splash decreased the number of macroaggregates and increased the number of microaggregates in the surface layer of soil.

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Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics

Chakshu

Carson

Sazonov

et al. 2021

Numer Methods Biomed Eng

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Fractional flow reserve (FFR) provides the functional relevance of coronary atheroma. The FFR-guided strategy has been shown to reduce unnecessary stenting, improve overall health outcome, and to be cost-saving. The noninvasive, coronary computerised tomography (CT) angiography-derived FFR (cFFR) is an emerging method in reducing invasive catheter based measurements. This computational fluid dynamics-based method is laborious as it requires expertise in multidisciplinary analysis of combining image analysis and computational mechanics. In this work, we present a rapid method, powered by unsupervised learning, to automatically calculate cFFR from CT scans without manual intervention.

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ImagePy: an open-source, Python-based and platform-independent software package for bioimage analysis

Abstract: ImagePy is free and open source software, with documentation and code available at https://github.com/Image-Py/imagepy under the BSD license. It has been tested on the Windows, Mac and Linux operating systems.

Cited by 38 publications

References 12 publications

PartSeg, a Tool for Quantitative Feature Extraction From 3D Microscopy Images for Dummies

PartSeg, a Tool for Quantitative Feature Extraction From 3D Microscopy Images for Dummies

Impact of raindrop sizes and intensities on the microcharacteristics of soil aggregates

Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics

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