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Ossinger, A., Bajic, A., Pan, S., Andersson, B., Ranefall, P., Hailer, N. P. & Schizas, N. (2019). A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning.. Journal of Neuroscience Methods, Article ID 108522.
Open this publication in new window or tab >>A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning.
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2019 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, article id 108522Article in journal (Refereed) Epub ahead of print
Abstract [en]

BACKGROUND: Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.

NEW METHOD: We developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.

RESULTS: NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.

COMPARISON WITH EXISTING METHODS: The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 minutes. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.

CONCLUSION: The developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.

Keywords
Adaptive threshold, Axonal outgrowth, Global threshold, Machine learning, Ramification index
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-397193 (URN)10.1016/j.jneumeth.2019.108522 (DOI)31734324 (PubMedID)
Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2019-11-18
Bengtsson, E. & Ranefall, P. (2019). Image analysis in digital pathology: Combining automated assessment of Ki67 staining quality with calculation of Ki67 cell proliferation index. Cytometry Part A, 95(7), 714-716
Open this publication in new window or tab >>Image analysis in digital pathology: Combining automated assessment of Ki67 staining quality with calculation of Ki67 cell proliferation index
2019 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 7, p. 714-716Article in journal, Editorial material (Other academic) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-393609 (URN)10.1002/cyto.a.23685 (DOI)000478855500004 ()30512236 (PubMedID)
Available from: 2018-12-03 Created: 2019-09-25 Last updated: 2019-09-26Bibliographically approved
Wu, C.-C., Klaesson, A., Buskas, J., Ranefall, P., Mirzazadeh, R., Söderberg, O. & Wolf, J. B. W. (2019). In situ quantification of individual mRNA transcripts in melanocytes discloses gene regulation of relevance to speciation. Journal of Experimental Biology, 222(5)
Open this publication in new window or tab >>In situ quantification of individual mRNA transcripts in melanocytes discloses gene regulation of relevance to speciation
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2019 (English)In: Journal of Experimental Biology, ISSN 0022-0949, E-ISSN 1477-9145, Vol. 222, no 5Article in journal (Refereed) Published
National Category
Genetics
Identifiers
urn:nbn:se:uu:diva-381095 (URN)10.1242/jeb.194431 (DOI)000461414600021 ()30718374 (PubMedID)
Funder
EU, European Research Council, ERCStG-336536Swedish Research Council, 621-2013-4510Knut and Alice Wallenberg Foundation
Available from: 2019-03-08 Created: 2019-04-15 Last updated: 2019-05-07Bibliographically approved
Wang, Y., Wang, C., Ranefall, P., Broussard, G. J., Wang, Y., Shi, G., . . . Yu, G. (2019). SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images. Bioinformatics
Open this publication in new window or tab >>SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images
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2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811Article in journal (Refereed) Published
Abstract [en]

Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real data sets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.Supplementary data are available at Bioinformatics online. Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.

National Category
Computer and Information Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-395144 (URN)10.1093/bioinformatics/btz760 (DOI)
Note

btz760

Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2019-10-14
Gibbs, A., Buggert, M., Edfeldt, G., Ranefall, P., Introini, A., Cheuk, S., . . . Tjernlund, A. (2018). Human Immunodeficiency Virus-Infected Women Have High Numbers of CD103-CD8+ T Cells Residing Close to the Basal Membrane of the Ectocervical Epithelium. Journal of Infectious Diseases, 218(3), 453-465
Open this publication in new window or tab >>Human Immunodeficiency Virus-Infected Women Have High Numbers of CD103-CD8+ T Cells Residing Close to the Basal Membrane of the Ectocervical Epithelium
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2018 (English)In: Journal of Infectious Diseases, ISSN 0022-1899, E-ISSN 1537-6613, Vol. 218, no 3, p. 453-465Article in journal (Refereed) Published
National Category
Infectious Medicine
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-361497 (URN)10.1093/infdis/jix661 (DOI)000439174200014 ()29272532 (PubMedID)
Available from: 2017-12-20 Created: 2018-09-26 Last updated: 2018-09-27Bibliographically approved
Bombrun, M., Ranefall, P. & Wählby, C. (2017). A web application to analyse and visualize digital images at multiple resolutions. In: : . Paper presented at 3rd Digital Pathology Congress.
Open this publication in new window or tab >>A web application to analyse and visualize digital images at multiple resolutions
2017 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Computerised image processing and automated quantification of cell and tissue morphology are becoming important tools for complementing visual assessment when investigating disease and/or drug response. The distribution and organisation of cells in intact tissue samples provides a rich visual-cognitive combination of information at multiple resolutions. The lowest magnification describes specific architectural patterns in the global tissue organization. At the same time, new methods for in situ sequencing of RNA allows profiling of gene expression at cellular resolution. Analysis at multiple resolutions thus opens up for large-scale comparison of genotype and phenotype. Expressed genes are locally amplified by molecular probes and rolling circle amplification, and decoded by repeating the sequencing cycle for the four letters of the genetic code. Using image processing methodologies on these giga-pixel images (40000 x 48000 pixels), we have identified more than 40 genes in parallel in the same tissue sample. Here, we present an open-source tool which combines the quantification of cell and tissue morphology with the analysis of gene expression. Our framework builds on CellProfiler, a free and open-source software developed for image based screening, and our viewing platform allow experts to visualize both gene expression patterns and quantitative measurements of tissue morphology with different overlays, such as the commonly used H&E staining. Furthermore, the user can draw regions of interest and extract local statistics on gene expression and tissue morphology over large slide scanner images at different resolutions. The TissueMaps platform provides a flexible solution to support the future development of histopathology, both as a diagnostic tool and as a research field.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-309623 (URN)
Conference
3rd Digital Pathology Congress
Available from: 2016-12-06 Created: 2016-12-06 Last updated: 2016-12-21
Kecheril Sadanandan, S., Ranefall, P., Le Guyader, S. & Wählby, C. (2017). Automated training of deep convolutional neural networks for cell segmentation. Scientific Reports, 7, Article ID 7860.
Open this publication in new window or tab >>Automated training of deep convolutional neural networks for cell segmentation
2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 7860Article in journal (Refereed) Published
Abstract [en]

Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329301 (URN)10.1038/s41598-017-07599-6 (DOI)000407400500092 ()28798336 (PubMedID)
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 682810eSSENCE - An eScience Collaboration
Available from: 2017-08-10 Created: 2017-09-12 Last updated: 2018-09-04Bibliographically approved
Bombrun, M., Ranefall, P., Lindblad, J., Allalou, A., Partel, G., Solorzano, L., . . . Wählby, C. (2017). Decoding gene expression in 2D and 3D. In: Image Analysis: Part II. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 257-268). Springer
Open this publication in new window or tab >>Decoding gene expression in 2D and 3D
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2017 (English)In: Image Analysis: Part II, Springer, 2017, p. 257-268Conference paper, Published paper (Refereed)
Abstract [en]

Image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical “slicing” by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science ; 10270
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-333686 (URN)10.1007/978-3-319-59129-2_22 (DOI)000454360300022 ()978-3-319-59128-5 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Projects
TissueMaps
Funder
EU, European Research Council, 682810
Available from: 2017-05-19 Created: 2017-11-16 Last updated: 2019-02-27Bibliographically approved
Gibbs, A., Buggert, M., Edfeldt, G., Ranefall, P., Introini, A., Cheuk, S., . . . Tjernlund, A. (2017). Increased numbers of CD103-CD8+ TRM cells in the cervical mucosa of HIV-infected women. Scandinavian Journal of Immunology, 86(4), 288-289
Open this publication in new window or tab >>Increased numbers of CD103-CD8+ TRM cells in the cervical mucosa of HIV-infected women
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2017 (English)In: Scandinavian Journal of Immunology, ISSN 0300-9475, E-ISSN 1365-3083, Vol. 86, no 4, p. 288-289Article in journal, Meeting abstract (Other academic) Published
National Category
Immunology in the medical area
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-346960 (URN)10.1111/sji.12587 (DOI)000411865200098 ()
Available from: 2017-09-27 Created: 2018-03-23 Last updated: 2018-03-27Bibliographically approved
Bombrun, M., Gao, H., Ranefall, P., Mejhert, N., Arner, P. & Wählby, C. (2017). Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models. Cytometry Part A, 91(11), 1068-1077
Open this publication in new window or tab >>Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models
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2017 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91, no 11, p. 1068-1077Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-333687 (URN)10.1002/cyto.a.23265 (DOI)000415885300005 ()
Funder
Swedish Research Council, 2012-4968
Available from: 2017-10-14 Created: 2017-11-16 Last updated: 2018-03-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6699-4015

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