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Wählby, Carolina
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Publications (10 of 83) Show all publications
Matuszewski, D. J., Hast, A., Wählby, C. & Sintorn, I.-M. (2017). A short feature vector for image matching: The Log-Polar Magnitude feature descriptor. PLoS ONE, 12(11), Article ID e0188496.
Open this publication in new window or tab >>A short feature vector for image matching: The Log-Polar Magnitude feature descriptor
2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, e0188496Article in journal (Refereed) Published
Abstract [en]

The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images.

National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:uu:diva-335460 (URN)10.1371/journal.pone.0188496 (DOI)
Funder
EU, European Research Council, ERC-CoG-2015Swedish Research Council, 2014-6075
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2017-12-14Bibliographically 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, 7860Article in journal (Refereed) Published
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)28798336 (PubMedID)
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 682810
Available from: 2017-08-10 Created: 2017-09-12 Last updated: 2017-11-17
Mignardi, M., Ishaq, O., Qian, X. & Wählby, C. (2017). Bridging Histology and Bioinformatics: Computational analysis of spatially resolved transcriptomics. Proceedings of the IEEE, 105(3), 530-541.
Open this publication in new window or tab >>Bridging Histology and Bioinformatics: Computational analysis of spatially resolved transcriptomics
2017 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 105, no 3, 530-541 p.Article in journal (Refereed) Published
Abstract [en]

It is well known that cells in tissue display a large heterogeneity in gene expression due to differences in cell lineage origin and variation in the local environment. Traditional methods that analyze gene expression from bulk RNA extracts fail to accurately describe this heterogeneity because of their intrinsic limitation in cellular and spatial resolution. Also, information on histology in the form of tissue architecture and organization is lost in the process. Recently, new transcriptome-wide analysis technologies have enabled the study of RNA molecules directly in tissue samples, thus maintaining spatial resolution and complementing histological information with molecular information important for the understanding of many biological processes and potentially relevant for the clinical management of cancer patients. These new methods generally comprise three levels of analysis. At the first level, biochemical techniques are used to generate signals that can be imaged by different means of fluorescence microscopy. At the second level, images are subject to digital image processing and analysis in order to detect and identify the aforementioned signals. At the third level, the collected data are analyzed and transformed into interpretable information by statistical methods and visualization techniques relating them to each other, to spatial distribution, and to tissue morphology. In this review, we describe state-of-the-art techniques used at all three levels of analysis. Finally, we discuss future perspective in this fast-growing field of spatially resolved transcriptomics.

Keyword
Biomedical image processing, biomedical signal analysis, computer-aided analysis, genetics, image analysis, image processing
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-283723 (URN)10.1109/JPROC.2016.2538562 (DOI)000395894900011 ()
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceeSSENCE - An eScience CollaborationSwedish Research Council, 2012-4968 2014-00599
Available from: 2016-04-06 Created: 2016-04-14 Last updated: 2017-04-27Bibliographically 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, 257-268 p.Conference paper, Published paper (Refereed)
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)978-3-319-59128-5 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Projects
TissueMaps
Available from: 2017-05-19 Created: 2017-11-16 Last updated: 2017-11-26Bibliographically approved
Wieslander, H., Forslid, G., Bengtsson, E., Wählby, C., Hirsch, J.-M., Runow Stark, C. & Kecheril Sadanandan, S. (2017). Deep convolutional neural networks for detecting cellular changes due to malignancy. In: IEEE International Conference on Computer Vision: . Paper presented at ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.. .
Open this publication in new window or tab >>Deep convolutional neural networks for detecting cellular changes due to malignancy
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2017 (English)In: IEEE International Conference on Computer Vision, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329356 (URN)
Conference
ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.
Funder
EU, European Research Council, 682810Swedish Research Council, 2012-4968
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-11-17
Ishaq, O., Sadanandan, S. K. & Wählby, C. (2017). Deep Fish: Deep Learning-Based Classification of Zebrafish Deformation for High-Throughput Screening. Journal of Biomolecular Screening, 22(1), 102-107.
Open this publication in new window or tab >>Deep Fish: Deep Learning-Based Classification of Zebrafish Deformation for High-Throughput Screening
2017 (English)In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 22, no 1, 102-107 p.Article in journal (Refereed) Published
Abstract [en]

Zebrafish (Danio rerio) is an important vertebrate model organism in biomedical research, especially suitable for morphological screening due to its transparent body during early development. Deep learning has emerged as a dominant paradigm for data analysis and found a number of applications in computer vision and image analysis. Here we demonstrate the potential of a deep learning approach for accurate high-throughput classification of whole-body zebrafish deformations in multifish microwell plates. Deep learning uses the raw image data as an input, without the need of expert knowledge for feature design or optimization of the segmentation parameters. We trained the deep learning classifier on as few as 84 images (before data augmentation) and achieved a classification accuracy of 92.8% on an unseen test data set that is comparable to the previous state of the art (95%) based on user-specified segmentation and deformation metrics. Ablation studies by digitally removing whole fish or parts of the fish from the images revealed that the classifier learned discriminative features from the image foreground, and we observed that the deformations of the head region, rather than the visually apparent bent tail, were more important for good classification performance.

National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-309535 (URN)10.1177/1087057116667894 (DOI)000394206000012 ()27613194 (PubMedID)
Funder
Swedish Research Council, 2012-4968eSSENCE - An eScience Collaboration
Available from: 2016-12-05 Created: 2016-12-05 Last updated: 2017-11-17
Talebizadeh, N., Zhou Hagström, N., Yu, Z., Kronschläger, M., Söderberg, P. & Wählby, C. (2017). Objective automated quantification of fluorescence signal in histological sections of rat lens. Cytometry Part A, 91.
Open this publication in new window or tab >>Objective automated quantification of fluorescence signal in histological sections of rat lens
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2017 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91Article in journal (Refereed) Epub ahead of print
National Category
Ophthalmology Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-322627 (URN)10.1002/cyto.a.23131 (DOI)
Available from: 2017-05-11 Created: 2017-05-28 Last updated: 2017-06-01Bibliographically 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, 1068-1077 p.Article 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)
Funder
Swedish Research Council, 2012-4968
Available from: 2017-10-14 Created: 2017-11-16 Last updated: 2017-11-28Bibliographically approved
Kecheril Sadanandan, S., Karlsson, J. & Wählby, C. (2017). Spheroid segmentation using multiscale deep adversarial networks. In: IEEE International Conference on Computer Vision: . Paper presented at ICCV Workshop on Bioimage Computing, Venice, Italy, October 23, 2017.. .
Open this publication in new window or tab >>Spheroid segmentation using multiscale deep adversarial networks
2017 (English)In: IEEE International Conference on Computer Vision, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329355 (URN)
Conference
ICCV Workshop on Bioimage Computing, Venice, Italy, October 23, 2017.
Funder
Swedish Research Council, 2012-4968EU, European Research Council, 682810
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-11-17
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