Biological Imaging Software Tools

By Kevin W. Eliceiri, Michael R. Berthold, Ilya G. Goldberg, Luis Ibáñez, Bangalore S. Manjunath, M. Martone, Robert F. Murphy, Hanchuan Peng, A. Plant, B. Roysam and 4 more. Nature Methods, 2012

513 citations recorded by the source corpus.

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8189311e56e338af585590f00721cdb22483a491

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Citations in the corpus

The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, background): “8 This is important for a wide array of biological studies that include quantifying the proximity of fluorescent-labeled proteins, 9 tracking cell fates over time, 10 automating cell counting, 11 tracking invading cancer cells, 12 collecting whole-slide information, 13 quantifying and…”
Deep Learning for Imaging Flow Cytometry: (2016, methodology): “When trained on a continuous biological process using discrete class labels, 215 conventional machine learning often fail to resolve the continuum [4].”
Machine learning and computer vision approaches for phenotypic profiling (2017, background): “Several groups have pack-aged these sets of feature extractors with modular segmentation pipelines into software platforms that are publicly available and widely used (Eliceiri et al., 2012).”
ImageJ2: ImageJ for the next generation of scientific image data (2017, background): “The bioimage informatics field [4] is fortunate to have a wide range of software tools available in both commercial and open source arenas [5].”
Classifying and segmenting microscopy images with deep multiple instance learning (2015, background): “High-content screening (HCS) technologies that combine automated fluorescence microscopy with high-throughput biotechnology have become powerful systems for studying cell biology and for drug screening (Liberali et al., 2015; Singh et al., 2014).”
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018, background): “Many of these strategies are readily available in various bioimage software packages (11), including open source options such as CellPro ﬁ ler (12), Ilastik (10), and ImageJ/Fiji (13), facilitating their adoption in routine biological research.”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “More information about popular biomedical imaging software tools can be found in [324]–[326].”
The ImageJ ecosystem: an open platform for biomedical image analysis (2015, background): “REVIEW ARTICLE Molecular Reproduction & Development (2015) The ImageJ Ecosystem: An Open Platform for Biomedical Image Analysis”