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Wählby, Carolina, professor
Alternative names
Publications (10 of 99) Show all publications
Gupta, A., Harrison, P. J., Wieslander, H., Pielawski, N., Kartasalo, K., Partel, G., . . . Wählby, C. (2019). Deep Learning in Image Cytometry: A Review. Cytometry Part A, 95(6), 366-380
Open this publication in new window or tab >>Deep Learning in Image Cytometry: A Review
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2019 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 6, p. 366-380Article, review/survey (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-371631 (URN)10.1002/cyto.a.23701 (DOI)000466792700002 ()30565841 (PubMedID)
Funder
Swedish Foundation for Strategic Research , BD15-0008SB16-0046Swedish Research Council, 2014-6075EU, European Research Council, ERC-2015-CoG 683810
Available from: 2018-12-19 Created: 2018-12-21 Last updated: 2019-06-14Bibliographically approved
Runow Stark, C., Gustavsson, I., Gyllensten, U., Darai Ramqvist, E., Lindblad, J., Wählby, C., . . . Hirsch, J.-M. (2018). Brush Biopsy For HR-HPV Detection With FTA Card And AI For Cytology Analysis - A Viable Non-invasive Alternative. In: Bengt Hasséus (Ed.), EAOM2018: . Paper presented at 14th Biennial Congress of the European Association of Oral Medicine, Göteborg, Sweden, September 2018.
Open this publication in new window or tab >>Brush Biopsy For HR-HPV Detection With FTA Card And AI For Cytology Analysis - A Viable Non-invasive Alternative
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2018 (English)In: EAOM2018 / [ed] Bengt Hasséus, 2018Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Introduction: Oral cancer accounts for about 800-1,000 new cases each year in Sweden and the ratio of cancer related to high-risk human papillomavirus (HR-HPV) is increasing in the younger population due to changes in sexual habits. The most two frequent HR-HPV types 16 and 18 have both significant oncogenic potential.

Objectives: In this pilot study we evaluate two non-invasive automated methods; 1) detection of HR-HPV using FTA cards, and 2) image scanning of cytology for detection of premalignant lesions as well as eradicate the early stage of neoplasia.

Material and Methods: 160 patients with verified HR-HPV oropharyngeal cancer, previous ano-genital HR-HPV-infection or potentially malignant oral disorder were recruited for non-invasive brush sampling and analyzed with two validated automated methods both used in cervix cancer screening. For analysis of HR-HPV DNA the indicating FTA elute micro cardTM were used for dry collection, transportation and storage of the brush samples. For analysis of cell morphology changes an automated liquid base Cytology method (Preserve Cyt) combined with deep learning computer aided technique was used.

Results: Preliminary results show that the FTA-method is reliable and indicates that healthy and malignant brush samples can be separated by image analysis. 

Conclusions: With further development of these fully automated methods, it is possible to implement a National Screening Program of the oral mucosa, and thereby select patients for further investigation in order to find lesions with potential malignancy in an early stage. 

Keywords
cytometry, deep learning, oral cancer, image analysis, HPV
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367985 (URN)
Conference
14th Biennial Congress of the European Association of Oral Medicine, Göteborg, Sweden, September 2018
Funder
VINNOVA, 2017-02447
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2018-12-02
Bengtsson, E., Wieslander, H., Forslid, G., Wählby, C., Hirsch, J.-M., Runow Stark, C., . . . Lindblad, J. (2018). Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning. In: Andrea Cossarizza (Ed.), CYTO2018: . Paper presented at 33rd Congress of the International Society for Advancement of Cytometry.
Open this publication in new window or tab >>Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning
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2018 (English)In: CYTO2018 / [ed] Andrea Cossarizza, 2018Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Background: The incidence of oral cancer is increasing and it is effecting younger individuals. PAP smear-based screening, visual, and automated, have been used for decades, to successfully decrease the incidence of cervical cancer. Can similar methods be used for oral cancer screening? We have carried out a pilot study using neural networks for classifying cells, both from cervical cancer and oral cancer patients. The results which were reported from a technical point of view at the 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), were particularly interesting for the oral cancer cases, and we are currently collecting and analyzing samples from more patients. Methods: Samples were collected with a brush in the oral cavity and smeared on glass slides, stained, and prepared, according to standard PAP procedures. Images from the slides were digitized with a 0.35 micron pixel size, using focus stacks with 15 levels 0.4 micron apart. Between 245 and 2,123 cell nuclei were manually selected for analysis for each of 14 datasets, usually 2 datasets for each of the 6 cases, in total around 15,000 cells. A small region was cropped around each nucleus, and the best 2 adjacent focus layers in each direction were automatically found, thus creating images of 100x100x5 pixels. Nuclei were chosen with an aim to select well preserved free-lying cells, with no effort to specifically select diagnostic cells. We therefore had no ground truth on the cellular level, only on the patient level. Subsets of these images were used for training 2 sets of neural networks, created according to the ResNet and VGG architectures described in literature, to distinguish between cells from healthy persons, and those with precancerous lesions. The datasets were augmented through mirroring and 90 degrees rotations. The resulting networks were used to classify subsets of cells from different persons, than those in the training sets. This was repeated for a total of 5 folds. Results: The results were expressed as the percentage of cell nuclei that the neural networks indicated as positive. The percentage of positive cells from healthy persons was in the range 8% to 38%. The percentage of positive cells collected near the lesions was in the range 31% to 96%. The percentages from the healthy side of the oral cavity of patients with lesions ranged 37% to 89%. For each fold, it was possible to find a threshold for the number of positive cells that would correctly classify all patients as normal or positive, even for the samples taken from the healthy side of the oral cavity. The network based on the ResNet architecture showed slightly better performance than the VGG-based one. Conclusion: Our small pilot study indicates that malignancyassociated changes that can be detected by neural networks may exist among cells in the oral cavity of patients with precancerous lesions. We are currently collecting samples from more patients, and will present those results as well, with our poster at CYTO 2018.

Keywords
cytometry, deep learning, oral cancer, image analysis
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366820 (URN)
Conference
33rd Congress of the International Society for Advancement of Cytometry
Funder
VINNOVA
Available from: 2018-11-26 Created: 2018-11-26 Last updated: 2018-11-26
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
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
Holzwarth, K., Köhler, R., Philipsen, L., Tokoyoda, K., Ladyhina, V., Wählby, C., . . . Hauser, A. E. (2018). Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections. Cytometry Part A, 93(9), 876-888
Open this publication in new window or tab >>Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections
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2018 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 93, no 9, p. 876-888Article in journal (Refereed) Published
National Category
Cell and Molecular Biology Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-363627 (URN)10.1002/cyto.a.23526 (DOI)000445603300002 ()30107096 (PubMedID)
Available from: 2018-08-14 Created: 2018-10-25 Last updated: 2018-10-31Bibliographically approved
Zhang, H., Ericsson, M., Virtanen, M., Weström, S., Wählby, C., Vahlquist, A. & Törmä, H. (2018). Quantitative image analysis of protein expression and colocalisation in skin sections [Letter to the editor]. Experimental dermatology, 27(2), 196-199
Open this publication in new window or tab >>Quantitative image analysis of protein expression and colocalisation in skin sections
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2018 (English)In: Experimental dermatology, ISSN 0906-6705, E-ISSN 1600-0625, Vol. 27, no 2, p. 196-199Article in journal, Letter (Refereed) Published
Abstract [en]

Immunofluorescence (IF) and in situ proximity ligation assay (isPLA) are techniques that are used for in situ protein expression and colocalisation analysis, respectively. However, an efficient quantitative method to analyse both IF and isPLA staining on skin sections is lacking. Therefore, we developed a new method for semi-automatic quantitative layer-by-layer measurement of protein expression and colocalisation in skin sections using the free open-source software CellProfiler. As a proof of principle, IF and isPLA of ichthyosis-related proteins TGm-1 and SDR9C7 were examined. The results indicate that this new method can be used for protein expression and colocalisation analysis in skin sections.

National Category
Biomedical Laboratory Science/Technology Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-338544 (URN)10.1111/exd.13457 (DOI)000423679700014 ()
Projects
Inherited skin disorders
Funder
Swedish Research Council, K2013-57x-22309-3
Available from: 2018-01-09 Created: 2018-01-10 Last updated: 2019-02-21Bibliographically approved
Edfeldt, G., Lajoie, J., Röhl, M., Omollo, K., Wählby, C., Boily-Larouche, G., . . . Tjernlund, A. (2018). The Effect of DMPA Use on the Human Cervical Epithelium: Mechanisms Revealed by Image Analysis. Paper presented at HIV Research for Prevention Meeting (HIVR4P 2018), October 21–25, Madrid, Spain. AIDS Research and Human Retroviruses, 34(S1), 310-310
Open this publication in new window or tab >>The Effect of DMPA Use on the Human Cervical Epithelium: Mechanisms Revealed by Image Analysis
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2018 (English)In: AIDS Research and Human Retroviruses, ISSN 0889-2229, E-ISSN 1931-8405, Vol. 34, no S1, p. 310-310Article in journal, Meeting abstract (Other academic) Published
National Category
Microbiology in the medical area
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-370042 (URN)000448371600570 ()
Conference
HIV Research for Prevention Meeting (HIVR4P 2018), October 21–25, Madrid, Spain
Available from: 2018-10-22 Created: 2019-01-08 Last updated: 2019-01-10Bibliographically approved
Solorzano, L., Almeida, G., Mesquita, B., Martins, D., Oliveira, C. & Wählby, C. (2018). Whole Slide Image Registration for the Study of Tumor Heterogeneity. In: MICCAI 2018 - International Workshop on Ophthalmic Medical Image Analysis: OMIA 2018, COMPAY 2018: Computational Pathology and Ophthalmic Medical Image Analysis. Paper presented at 21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION,Miccai2018, September 16-20 2018 GRANADA / SPAIN (pp. 95-102). Spain: Springer
Open this publication in new window or tab >>Whole Slide Image Registration for the Study of Tumor Heterogeneity
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2018 (English)In: MICCAI 2018 - International Workshop on Ophthalmic Medical Image Analysis: OMIA 2018, COMPAY 2018: Computational Pathology and Ophthalmic Medical Image Analysis, Spain: Springer, 2018, p. 95-102Conference paper, Published paper (Refereed)
Abstract [en]

Consecutive thin sections of tissue samples make it possible to study local variation in e.g. protein expression and tumor heterogeneity by staining for a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thin sections result in artifacts that make global affine registration prone to very large local errors; (iii) Local affine registration is required to preserve correct tissue morphology (local size, shape and texture). In our approach we compare WSI registration based on automatic and manual feature selection on either the full image or natural sub-regions (as opposed to square tiles). Working with natural sub-regions, in an interactive tool makes it possible to exclude regions containing scientifically irrelevant information. We also present a new way to visualize local registration quality by a Registration Confidence Map (RCM). With this method, intra-tumor heterogeneity and characteristics of the tumor microenvironment can be observed and quantified.

Place, publisher, year, edition, pages
Spain: Springer, 2018
Series
Lecture Notes in Computer Science (LNCS) ; 11039
Keywords
WSI, digital pathology
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368006 (URN)10.1007/978-3-030-00949-6_12 (DOI)978-3-030-00949-6 (ISBN)
Conference
21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION,Miccai2018, September 16-20 2018 GRANADA / SPAIN
Projects
TissUUmaps
Available from: 2018-12-02 Created: 2018-12-02 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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