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Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala university.ORCID iD: 0000-0002-1636-3469
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
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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.

Place, publisher, year, edition, pages
2018.
Keywords [en]
cytometry, deep learning, oral cancer, image analysis
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-366820OAI: oai:DiVA.org:uu-366820DiVA, id: diva2:1265836
Conference
33rd Congress of the International Society for Advancement of Cytometry
Funder
VINNOVAAvailable from: 2018-11-26 Created: 2018-11-26 Last updated: 2018-11-26

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http://cytoconference.org/2018/Abstracts-and-Awards/Program-Abstracts/CYTO-2018-Abstract-V7-FINALb.aspx

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Bengtsson, EwertWieslander, HåkanWählby, CarolinaHirsch, Jan-MichaelKecheril Sadanandan, SajithLindblad, Joakim

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Bengtsson, EwertWieslander, HåkanWählby, CarolinaHirsch, Jan-MichaelKecheril Sadanandan, SajithLindblad, Joakim
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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionAutomatic controlScience for Life Laboratory, SciLifeLabOral and Maxillofacial Surgery
Medical Image Processing

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