Erratum: Visualization of image data from cells to organisms

Walter, Thomas; Shattuck, David W.; Baldock, Richard; Carpenter, Anne E.; Duce, Suzanne L.; Ellenberg, Jan; Fraser, Adam; Hamilton, Nicholas A.; Pieper, Steve; Ragan, Mark A.; Schneider, Jürgen; Tomančák, Pavel; Hériché, Jean‐Karim

doi:10.1038/nmeth0610-479a

Cited by 6 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complexity inherent in biological, imaging and other types of sensing data has motivated application of a variety of statistical and computational methods, ranging from artificial neural networks [22] to visualization techniques [23,24]. In a number of cases, the data are generated by a wide range of sensing devices, obtained by an equally large variety of sensor types.…”

Section: Trends In the Use Of Data Analysis Methodsmentioning

confidence: 99%

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

Oliveira¹,

Pavinatto²,

Constantino³

et al. 2012

Biointerphases

View full text Add to dashboard Cite

An overview is provided of the various methods for analyzing biosensing data, with emphasis on information visualization approaches such as multidimensional projection techniques. Emphasis is placed on the importance of data analysis methods, with a description of traditional techniques, including the advantages and limitations of linear and non-linear methods to generate layouts that emphasize similarity/dissimilarity relationships among data instances. Particularly important are recent methods that allow processing high-dimensional data, thus taking full advantage of the capabilities of modern equipment. In this area, now referred to as e-science, the choice of appropriate data analysis methods is crucial to enhance the sensitivity and selectivity of sensors and biosensors. Two types of systems deserving attention in this context are electronic noses and electronic tongues, which are made of sensor arrays whose electrical or electrochemical responses are combined to provide “finger print” information for aromas and tastes. Examples will also be given of unprecedented detection of tropical diseases, made possible with the use of multidimensional projection techniques. Furthermore, ways of using these techniques along with other information visualization methods to optimize biosensors will be discussed.

show abstract

Section: Trends In the Use Of Data Analysis Methodsmentioning

confidence: 99%

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

Oliveira¹,

Pavinatto²,

Constantino³

et al. 2012

Biointerphases

View full text Add to dashboard Cite

show abstract

“…However, such scans comprise more than 10 TB of image data (Li et al, 2019), making them too large to be processed by typical computers. The large size of mesoscopic whole-brain data has posed a significant challenge to efficient visualization of and interaction with these datasets (Helmstaedter and Mitra, 2012), which are basic and essential tasks in the bioimage analysis pipeline (Peng, 2008;Walter et al, 2010;Meijering et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

webTDat: A Web-Based, Real-Time, 3D Visualization Framework for Mesoscopic Whole-Brain Images

et al. 2021

Front. Neuroinform.

View full text Add to dashboard Cite

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.

show abstract

Complementary X-ray tomography techniques for histology-validated 3D imaging of soft and hard tissues using plaque-containing blood vessels as examples

et al. 2014

View full text Add to dashboard Cite

A key problem in X-ray computed tomography is choosing photon energies for postmortem specimens containing both soft and hard tissues. Increasing X-ray energy reduces image artifacts from highly absorbing hard tissues including plaque, but it simultaneously decreases contrast in soft tissues including the endothelium. Therefore, identifying the lumen within plaque-containing vessels is challenging. Destructive histology, the gold standard for tissue evaluation, reaches submicron resolution in two dimensions, whereas slice thickness limits spatial resolution in the third. We present a protocol to systematically analyze heterogeneous tissues containing weakly and highly absorbing components in the original wet state, postmortem. Taking the example of atherosclerotic human coronary arteries, the successively acquired 3D data of benchtop and synchrotron radiation-based tomography are validated by histology. The entire protocol requires ∼20 working days, enables differentiation between plaque, muscle and fat tissues without using contrast agents and permits blood flow simulations in vessels with plaque-induced constrictions.

show abstract

Erratum: Visualization of image data from cells to organisms

Abstract: In the version of this article initially published, Carl Zeiss Microimaging was not acknowledged for providing access to the SPIM prototype used to generate images in the article. The error has been corrected in the HTML and PDF versions of the article.

Cited by 6 publications

References 0 publications

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

webTDat: A Web-Based, Real-Time, 3D Visualization Framework for Mesoscopic Whole-Brain Images

Complementary X-ray tomography techniques for histology-validated 3D imaging of soft and hard tissues using plaque-containing blood vessels as examples

Contact Info

Product

Resources

About