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Hirsch, Jan-Michael
Publications (4 of 4) 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
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
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
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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
Nowinski, D., Messo, E., Hedlund, A. & Hirsch, J.-M. (2011). Computer-navigated contouring of craniofacial fibrous dysplasia involving the orbit. The Journal of craniofacial surgery (Print), 22(2), 469-472
Open this publication in new window or tab >>Computer-navigated contouring of craniofacial fibrous dysplasia involving the orbit
2011 (English)In: The Journal of craniofacial surgery (Print), ISSN 1049-2275, E-ISSN 1536-3732, Vol. 22, no 2, p. 469-472Article in journal (Refereed) Published
Abstract [en]

Virtual surgical planning and computer-aided surgery were used to treat a mono-ostotic fibrous dysplasia of the right zygoma. Mirroring of the contralateral zygoma sets the target for the contouring of the affected zygomatic bone. An optical system for computer-guided surgery was used. Instruments were calibrated and visualized in real time on screen. Achievement of the virtually set target for the orbitozygomatic anatomy was assessed during surgery. Postoperative computed tomography and clinical follow-up confirmed an excellent result with regard to facial symmetry and eye bulb position. The volume of the orbit was increased from 24.2 to 26.0 mL compared with a contralateral orbital volume of 25.7 mL. Computer-guided surgery may be a useful tool in the surgical reduction of craniofacial fibrous dysplasia.

Keywords
Computer-assisted surgery, orbit, virtual planning
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-151519 (URN)10.1097/SCS.0b013e3182074312 (DOI)000288535800022 ()21403578 (PubMedID)
Available from: 2011-04-13 Created: 2011-04-13 Last updated: 2017-12-11Bibliographically approved
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