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Publications (10 of 78) Show all publications
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
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: International Symposium on Biomedical Imaging (ISBI 2018): . Paper presented at IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, D.C, USA, April 2018 (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: International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, p. 921-925Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367040 (URN)10.1109/ISBI.2018.8363721 (DOI)
Conference
IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, D.C, USA, April 2018
Funder
VINNOVA, 2016-02329
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2018-12-18
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: 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
Öfverstedt, J., Sladoje, N. & Lindblad, J. (2018). Distance Between Vector-valued Images based on Intersection Decomposition with Applications in Object Detection. In: Swedish Symposium on Image Analysis: . Paper presented at SSBA 2018, Stockholm, Sweden, March 2018.
Open this publication in new window or tab >>Distance Between Vector-valued Images based on Intersection Decomposition with Applications in Object Detection
2018 (English)In: Swedish Symposium on Image Analysis, 2018Conference paper, Published paper (Other academic)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367997 (URN)
Conference
SSBA 2018, Stockholm, Sweden, March 2018
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2018-12-07
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2018). Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information. arXiv
Open this publication in new window or tab >>Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information
2018 (English)In: arXiv, ISSN 2331-8422Article in journal (Other academic) Published
Abstract [en]

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367969 (URN)
Funder
VINNOVA, 2016-02329
Available from: 2018-12-01 Created: 2018-12-01 Last updated: 2018-12-07
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2018). Stochastic Distance Transform. arXiv
Open this publication in new window or tab >>Stochastic Distance Transform
2018 (English)In: arXiv, ISSN 2331-8422Article in journal (Other academic) Published
Abstract [en]

The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.

National Category
Computer Vision and Robotics (Autonomous Systems) Discrete Mathematics
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367970 (URN)
Available from: 2018-12-01 Created: 2018-12-01 Last updated: 2018-12-07
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 Swedish Symposium on Deep Learning.
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
Swedish Symposium on Deep Learning
Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2018-12-04
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. 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
Note

Paper in print

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2018-12-04
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2017). Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction. In: Swedish Symposium on Image Analysis: . Swedish Society for Automated Image Analysis
Open this publication in new window or tab >>Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction
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2017 (English)In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Swedish Society for Automated Image Analysis, 2017
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-335371 (URN)
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-08-24
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7312-8222

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