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Sintorn, Ida-Maria
Publications (10 of 58) Show all publications
Adler, J., Sintorn, I.-M., Strand, R. & Parmryd, I. (2019). Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous. COMMUNICATIONS BIOLOGY, 2, Article ID 12.
Open this publication in new window or tab >>Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous
2019 (English)In: COMMUNICATIONS BIOLOGY, ISSN 2399-3642, Vol. 2, article id 12Article in journal (Refereed) Published
Abstract [en]

Cells are neither flat nor smooth, which has serious implications for prevailing plasma membrane models and cellular processes like cell signalling, adhesion and molecular clustering. Using probability distributions from diffusion simulations, we demonstrate that 2D and 3D Euclidean distance measurements substantially underestimate diffusion on non-flat surfaces. Intuitively, the shortest within surface distance (SWSD), the geodesic distance, should reduce this problem. The SWSD is accurate for foldable surfaces but, although it outperforms 2D and 3D Euclidean measurements, it still underestimates movement on deformed surfaces. We demonstrate that the reason behind the underestimation is that topographical features themselves can produce both super- and subdiffusion, i.e. the appearance of anomalous diffusion. Differentiating between topography-induced and genuine anomalous diffusion requires characterising the surface by simulating Brownian motion on high-resolution cell surface images and a comparison with the experimental data.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019
National Category
Biophysics
Identifiers
urn:nbn:se:uu:diva-380506 (URN)10.1038/s42003-018-0240-2 (DOI)000461148000001 ()30652124 (PubMedID)
Funder
Swedish Research Council, 2015-04764Swedish Research Council, 2014-6075
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-04-15Bibliographically approved
Hast, A., Sablina, V. A., Sintorn, I.-M. & Kylberg, G. (2018). A fast Fourier based feature descriptor and a cascade nearest neighbour search with an efficient matching pipeline for mosaicing of microscopy images. Pattern Recognition and Image Analysis, 28(2), 261-272
Open this publication in new window or tab >>A fast Fourier based feature descriptor and a cascade nearest neighbour search with an efficient matching pipeline for mosaicing of microscopy images
2018 (English)In: Pattern Recognition and Image Analysis, ISSN 1054-6618, Vol. 28, no 2, p. 261-272Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-354147 (URN)10.1134/S1054661818020050 (DOI)
Available from: 2018-06-16 Created: 2018-06-19 Last updated: 2018-06-20Bibliographically approved
Gupta, A., Harrison, P. J., Wieslander, H., Pielawski, N., Kartasalo, K., Partel, G., . . . Wählby, C. (2018). Deep Learning in Image Cytometry: A Review.. Cytometry Part A
Open this publication in new window or tab >>Deep Learning in Image Cytometry: A Review.
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2018 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930Article in journal (Refereed) Epub ahead of print
Abstract [en]

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Keywords
biomedical image analysis, cell analysis, convolutional neural networks, deep learning, image cytometry, machine learning, microscopy
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-371631 (URN)10.1002/cyto.a.23701 (DOI)30565841 (PubMedID)
Funder
Swedish Foundation for Strategic Research
Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2019-03-28
Bajic, B., Suveer, A., Gupta, A., Pepic, I., Lindblad, J., Sladoje, N. & Sintorn, I.-M. (2018). Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement. In: Proc. 15th International Symposium on Biomedical Imaging: . Paper presented at ISBI 2018, April 4–7, Washington, DC (pp. 921-925). IEEE
Open this publication in new window or tab >>Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement
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2018 (English)In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 921-925Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367040 (URN)10.1109/ISBI.2018.8363721 (DOI)000455045600210 ()978-1-5386-3636-7 (ISBN)
Conference
ISBI 2018, April 4–7, Washington, DC
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-04-17Bibliographically approved
Gupta, A., Suveer, A., Bajic, B., Pepic, I., Lindblad, J., Sladoje, N. & Sintorn, I.-M. (2018). Denoising of Short Exposure Transmission Electron Microscopy Images using CNN. In: Swedish Symposium on Image Analysis: . Paper presented at SSBA2018, Stockholm, Sweden, March 2018.
Open this publication in new window or tab >>Denoising of Short Exposure Transmission Electron Microscopy Images using CNN
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2018 (English)In: Swedish Symposium on Image Analysis, 2018Conference paper, Published paper (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367996 (URN)
Conference
SSBA2018, Stockholm, Sweden, March 2018
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2019-03-06Bibliographically approved
Matuszewski, D. J., Wählby, C., Krona, C., Nelander, S. & Sintorn, I.-M. (2018). Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma. SLAS Discovery: Advancing Life Sciences R&D, 23(10), 1030-1039
Open this publication in new window or tab >>Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma
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2018 (English)In: SLAS Discovery: Advancing Life Sciences R&D, ISSN 2472-5552, Vol. 23, no 10, p. 1030-1039Article in journal (Refereed) Published
Abstract [en]

Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368698 (URN)10.1177/2472555218791414 (DOI)000452283500003 ()30074852 (PubMedID)
Funder
AstraZenecaSwedish Research Council, 2012-4968; 2014-6075eSSENCE - An eScience Collaboration
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-01-22Bibliographically approved
Matuszewski, D. J. & Sintorn, I.-M. (2018). Minimal annotation training for segmentation of microscopy images. In: Proc. 15th International Symposium on Biomedical Imaging: . Paper presented at ISBI 2018, April 4–7, Washington, DC (pp. 387-390). IEEE
Open this publication in new window or tab >>Minimal annotation training for segmentation of microscopy images
2018 (English)In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 387-390Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368701 (URN)10.1109/ISBI.2018.8363599 (DOI)000455045600088 ()978-1-5386-3636-7 (ISBN)
Conference
ISBI 2018, April 4–7, Washington, DC
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-02-21Bibliographically approved
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2018). Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps. In: Swedish Symposium on Deep Learning: . Paper presented at 2nd Swedish Symposium on Deep Learning, 5-6 September, 2018,Göteborg, Sweden.
Open this publication in new window or tab >>Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps
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2018 (English)In: Swedish Symposium on Deep Learning, 2018Conference paper, Oral presentation with published abstract (Other academic)
Keywords
Machine learning
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368016 (URN)
Conference
2nd Swedish Symposium on Deep Learning, 5-6 September, 2018,Göteborg, Sweden
Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-03-14Bibliographically approved
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2018). Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps. In: Workshop on BioImage Computing @ ECCV 2018: . Paper presented at European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany. Springer
Open this publication in new window or tab >>Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps
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2018 (English)In: Workshop on BioImage Computing @ ECCV 2018, Springer, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensity-based ones at a late classification stage.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Texture Analysis, Convolutional Neural Networks, Machine learning
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368015 (URN)
Conference
European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany
Note

Paper in print

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-03-14Bibliographically approved
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, article id 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)000416841900060 ()
Funder
EU, European Research Council, ERC-CoG-2015Swedish Research Council, 2014-6075
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2019-01-22Bibliographically approved
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