1
|
Verschuuren M, De Vylder J, Catrysse H, Robijns J, Philips W, De Vos WH. Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification. PLoS One 2017; 12:e0170688. [PMID: 28125723 PMCID: PMC5268651 DOI: 10.1371/journal.pone.0170688] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 01/09/2017] [Indexed: 11/19/2022] Open
Abstract
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.
Collapse
Affiliation(s)
| | - Jonas De Vylder
- Department of Telecommunication and Information Processing, IPI, iMinds, Ghent University, Ghent, Belgium
| | - Hannes Catrysse
- Institute for Agricultural and Fisheries Research (ILVO), Melle, Belgium
| | - Joke Robijns
- Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Wilfried Philips
- Department of Telecommunication and Information Processing, IPI, iMinds, Ghent University, Ghent, Belgium
| | - Winnok H. De Vos
- Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
- Department of Molecular Biotechnology, Ghent University, Ghent, Belgium
- * E-mail:
| |
Collapse
|
2
|
Basset A, Boulanger J, Salamero J, Bouthemy P, Kervrann C. Adaptive Spot Detection With Optimal Scale Selection in Fluorescence Microscopy Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:4512-4527. [PMID: 26353357 DOI: 10.1109/tip.2015.2450996] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurately detecting subcellular particles in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking, or classification. Our primary goal is to segment vesicles likely to share nearly the same size in fluorescence microscopy images. Our method termed adaptive thresholding of Laplacian of Gaussian (LoG) images with autoselected scale (ATLAS) automatically selects the optimal scale corresponding to the most frequent spot size in the image. Four criteria are proposed and compared to determine the optimal scale in a scale-space framework. Then, the segmentation stage amounts to thresholding the LoG of the intensity image. In contrast to other methods, the threshold is locally adapted given a probability of false alarm (PFA) specified by the user for the whole set of images to be processed. The local threshold is automatically derived from the PFA value and local image statistics estimated in a window whose size is not a critical parameter. We also propose a new data set for benchmarking, consisting of six collections of one hundred images each, which exploits backgrounds extracted from real microscopy images. We have carried out an extensive comparative evaluation on several data sets with ground-truth, which demonstrates that ATLAS outperforms existing methods. ATLAS does not need any fine parameter tuning and requires very low computation time. Convincing results are also reported on real total internal reflection fluorescence microscopy images.
Collapse
|
3
|
Ball DA, Lux MW, Adames NR, Peccoud J. Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology. PLoS One 2014; 9:e107087. [PMID: 25210731 PMCID: PMC4161401 DOI: 10.1371/journal.pone.0107087] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 07/30/2014] [Indexed: 11/19/2022] Open
Abstract
The use of microfluidics in live cell imaging allows the acquisition of dense time-series from individual cells that can be perturbed through computer-controlled changes of growth medium. Systems and synthetic biologists frequently perform gene expression studies that require changes in growth conditions to characterize the stability of switches, the transfer function of a genetic device, or the oscillations of gene networks. It is rarely possible to know a priori at what times the various changes should be made, and the success of the experiment is unknown until all of the image processing is completed well after the completion of the experiment. This results in wasted time and resources, due to the need to repeat the experiment to fine-tune the imaging parameters. To overcome this limitation, we have developed an adaptive imaging platform called GenoSIGHT that processes images as they are recorded, and uses the resulting data to make real-time adjustments to experimental conditions. We have validated this closed-loop control of the experiment using galactose-inducible expression of the yellow fluorescent protein Venus in Saccharomyces cerevisiae. We show that adaptive imaging improves the reproducibility of gene expression data resulting in more accurate estimates of gene network parameters while increasing productivity ten-fold.
Collapse
Affiliation(s)
- David A. Ball
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, United States of America
| | - Matthew W. Lux
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, United States of America
| | - Neil R. Adames
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, United States of America
| | - Jean Peccoud
- Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, United States of America
| |
Collapse
|
4
|
Eliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath B, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B, Stuurman N, Swedlow JR, Tomancak P, Carpenter AE. Biological imaging software tools. Nat Methods 2012; 9:697-710. [PMID: 22743775 PMCID: PMC3659807 DOI: 10.1038/nmeth.2084] [Citation(s) in RCA: 337] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
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.
Collapse
Affiliation(s)
| | - Michael R. Berthold
- Department of Computer and Information Science, Universität Konstanz, Konstanz, Germany
| | | | | | - B.S. Manjunath
- Center for Bio-image Informatics, Department of Electrical and Computer Engineering, University of California, Santa Barbara, California, USA
| | - Maryann E. Martone
- National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, California USA
| | - Robert F. Murphy
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Hanchuan Peng
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia USA
| | - Anne L. Plant
- Biochemical Science Division, NIST, Gaithersburg, Maryland, USA
| | - Badrinath Roysam
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas USA
| | - Nico Stuurman
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, USA
| | - Jason R. Swedlow
- Wellcome Trust Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Pavel Tomancak
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | |
Collapse
|
5
|
Abstract
As microscopy becomes increasingly automated and imaging expands in the spatial and time dimensions, quantitative analysis tools for fluorescent imaging are becoming critical to remove both bottlenecks in throughput as well as fully extract and exploit the information contained in the imaging. In recent years there has been a flurry of activity in the development of bio-image analysis tools and methods with the result that there are now many high-quality, well-documented, and well-supported open source bio-image analysis projects with large user bases that cover essentially every aspect from image capture to publication. These open source solutions are now providing a viable alternative to commercial solutions. More importantly, they are forming an interoperable and interconnected network of tools that allow data and analysis methods to be shared between many of the major projects. Just as researchers build on, transmit, and verify knowledge through publication, open source analysis methods and software are creating a foundation that can be built upon, transmitted, and verified. Here we describe many of the major projects, their capabilities, and features. We also give an overview of the current state of open source software for fluorescent microscopy analysis and the many reasons to use and develop open source methods.
Collapse
|
6
|
Aguiar-Pulido V, Munteanu CR, Seoane JA, Fernández-Blanco E, Pérez-Montoto LG, González-Díaz H, Dorado J. Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer. MOLECULAR BIOSYSTEMS 2012; 8:1716-22. [PMID: 22466084 DOI: 10.1039/c2mb25039j] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Fast cancer diagnosis represents a real necessity in applied medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because it permits us to numerically describe any real system such as the protein macromolecules by transforming real properties into molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naïve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.
Collapse
Affiliation(s)
- Vanessa Aguiar-Pulido
- Department of Information and Communications Technologies, University of A Coruña, A Coruña, Spain
| | | | | | | | | | | | | |
Collapse
|