uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 111) 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
Show others...
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
Show others...
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
Lidayová, K., Gupta, A., Frimmel, H., Sintorn, I.-M., Bengtsson, E. & Smedby, Ö. (2017). Classification of cross-sections for vascular skeleton extraction using convolutional neural networks. In: Medical Image Understanding and Analysis: . Paper presented at MIUA 2017, July 11–13, Edinburgh, UK (pp. 182-194). Springer
Open this publication in new window or tab >>Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
Show others...
2017 (English)In: Medical Image Understanding and Analysis, Springer, 2017, p. 182-194Conference paper, Published paper (Refereed)
Abstract
Place, publisher, year, edition, pages
Springer, 2017
Series
Communications in Computer and Information Science ; 723
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-318529 (URN)10.1007/978-3-319-60964-5_16 (DOI)978-3-319-60963-8 (ISBN)
Conference
MIUA 2017, July 11–13, Edinburgh, UK
Available from: 2017-06-22 Created: 2017-03-24 Last updated: 2018-07-03Bibliographically approved
Bengtsson, E., Danielsen, H., Treanor, D., Gurcan, M. N., MacAulay, C. & Molnár, B. (2017). Computer-aided diagnostics in digital pathology. Cytometry Part A, 91(6), 551-554
Open this publication in new window or tab >>Computer-aided diagnostics in digital pathology
Show others...
2017 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91, no 6, p. 551-554Article in journal, Editorial material (Other academic) Published
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-330045 (URN)10.1002/cyto.a.23151 (DOI)000404037600001 ()28640523 (PubMedID)
Available from: 2017-06-22 Created: 2017-09-25 Last updated: 2017-11-25Bibliographically approved
Wieslander, H., Forslid, G., Bengtsson, E., Wählby, C., Hirsch, J.-M., Runow Stark, C. & Sadanandan, S. K. (2017). Deep convolutional neural networks for detecting cellular changes due to malignancy. In: Proc. 16th International Conference on Computer Vision Workshops: . Paper presented at ICCV Workshop on Bioimage Computing, Venice, Italy, October 23, 2017 (pp. 82-89). IEEE Computer Society
Open this publication in new window or tab >>Deep convolutional neural networks for detecting cellular changes due to malignancy
Show others...
2017 (English)In: Proc. 16th International Conference on Computer Vision Workshops, IEEE Computer Society, 2017, p. 82-89Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2017
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329356 (URN)10.1109/ICCVW.2017.18 (DOI)000425239600011 ()978-1-5386-1034-3 (ISBN)
Conference
ICCV Workshop on Bioimage Computing, Venice, Italy, October 23, 2017
Funder
EU, European Research Council, 682810Swedish Research Council, 2012-4968
Available from: 2018-01-23 Created: 2017-09-13 Last updated: 2018-05-25Bibliographically approved
Bengtsson, E. (2017). Image processing and its hardware support. Analysis vs synthesis - historical trends. In: P Sharma, F M Bianchi (Ed.), LNCS 10269: SCIA 2017. Paper presented at 20th Scandinavian Conference on Image Analysis (pp. 3-14). Switzerland
Open this publication in new window or tab >>Image processing and its hardware support. Analysis vs synthesis - historical trends
2017 (English)In: LNCS 10269: SCIA 2017 / [ed] P Sharma, F M Bianchi, Switzerland, 2017, p. 3-14Conference paper, Published paper (Refereed)
Abstract [en]

Computers can be used to handle images in two fundamen-tally dierent ways. They can be used to analyse images to obtain quan-titative data or some image understanding. And they can be used tocreate images that can be displayed through computer graphics and vi-sualization. For both of these purposes it is of interest to develop ecientways of representing, compressing and storing the images. While SCIA,the Scandinavia Conference of Image Analysis, according to its name ismainly concerned with the former aspect of images, it is interesting tonote that image analysis throughout its history has been strongly in u-enced also by developments on the visualization side. When the confer-ence series now has reached its 20th milestone it may be worth re ectingon what factors have been important in forming the development of theeld. To understand where you are it is good to know where you comefrom and it may even help you understand where you are going.

Place, publisher, year, edition, pages
Switzerland: , 2017
Keywords
history, image processing, image analysis, computer graphics, visualization, hardware support
National Category
Engineering and Technology Computer Systems
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366917 (URN)10.1007/978-3-319-59126-1 (DOI)978-3-319-59126-1 (ISBN)978-3-319-59125-4 (ISBN)
Conference
20th Scandinavian Conference on Image Analysis
Available from: 2018-11-26 Created: 2018-11-26 Last updated: 2018-11-26
Lidayová, K., Frimmel, H., Bengtsson, E. & Smedby, Ö. (2017). Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation. Journal of Medical Imaging, 4, 024004:1-11
Open this publication in new window or tab >>Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
2017 (English)In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, p. 024004:1-11Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-318527 (URN)10.1117/1.JMI.4.2.024004 (DOI)000405944600018 ()28466028 (PubMedID)
Available from: 2017-04-28 Created: 2017-03-28 Last updated: 2017-11-17Bibliographically approved
Barrera, T., Hast, A. & Bengtsson, E. (2016). A chronological and mathematical overview of digital circle generation algorithms: Introducing efficient 4- and 8-connected circles. International Journal of Computer Mathematics, 93(8), 1241-1253
Open this publication in new window or tab >>A chronological and mathematical overview of digital circle generation algorithms: Introducing efficient 4- and 8-connected circles
2016 (English)In: International Journal of Computer Mathematics, ISSN 0020-7160, E-ISSN 1029-0265, Vol. 93, no 8, p. 1241-1253Article in journal (Refereed) Published
Abstract [en]

Circles are one of the basic drawing primitives for computers and while the naive way of setting up an equation for drawing circles is simple, implementing it in an efficient way using integer arithmetic has resulted in quite a few different algorithms. We present a short chronological overview of the most important publications of such digital circle generation algorithms. Bresenham is often assumed to have invented the first all integer circle algorithm. However, there were other algorithms published before his first official publication, which did not use floating point operations. Furthermore, we present both a 4- and an 8-connected all integer algorithm. Both of them proceed without any multiplication, using just one addition per iteration to compute the decision variable, which makes them more efficient than previously published algorithms.

Keywords
digital circle drawing; all integer arithmetic; connectivity; Bresenham; midpoint and Michener circle
National Category
Other Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-266119 (URN)10.1080/00207160.2015.1056170 (DOI)000377224200001 ()
Available from: 2015-06-24 Created: 2015-11-05 Last updated: 2018-01-10Bibliographically approved
Ranefall, P., Wählby, C. & Bengtsson, E. (2016). Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. In: : . Paper presented at 4th Nordic Symposium on Digital Pathology.
Open this publication in new window or tab >>Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections
2016 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Aim

This work describes a proof-of-principle study within the Exchange of Diagnostic Images in Networks (ExDIN) project, for automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. The idea was to mimic the manual grading process: “The assessment is carried out on invasive cancer within the area with the highest number of Ki67-positive cancer cell nuclei/area (hot spot), containing at least 200 cells.”

Method

  • Color deconvolution to separate the image into brown and blue channels.

  • Extract the 10 subsampled tiles (size corresponding to ~200 cells) with the highest values for pre-defined texture and color features.

  • Analyze these tiles in full resolution and compute the maximum positivity (defined as area of positive cells in relation to total cell area, rather than number of cells, since that will speed up the computations and avoid introducing errors due to over- or under segmentation of connected objects).

     

     

     

     

     

     

     

     

     

     

     

     

     

Figure 1. Illustration of the procedure. Hot spot candidates are extracted from low resolution tiles. Then the final hot spot is selected among the corresponding full resolution versions.

The results show good correlation to manual estimates and the procedure takes ~4 minutes/slide.

Future improvements

  • Rules and features defined using machine learning based on training samples given by pathologists.

  • User interface where suggested regions can be deselected manually.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-309606 (URN)
Conference
4th Nordic Symposium on Digital Pathology
Projects
ExDIN
Available from: 2016-12-06 Created: 2016-12-06 Last updated: 2016-12-21
Lidayová, K., Frimmel, H., Wang, C., Bengtsson, E. & Smedby, Ö. (2016). Fast vascular skeleton extraction algorithm. Pattern Recognition Letters, 76, 67-75
Open this publication in new window or tab >>Fast vascular skeleton extraction algorithm
Show others...
2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, p. 67-75Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-267146 (URN)10.1016/j.patrec.2015.06.024 (DOI)000375135600009 ()
Available from: 2015-07-09 Created: 2015-11-18 Last updated: 2017-12-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1636-3469

Search in DiVA

Show all publications