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Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.ORCID iD: 0000-0003-0908-9470
Region Östergötland, Center for Diagnostics, Department of Clinical Pathology and Clinical Genetics. Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
2014 (English)In: 2014 22nd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2014, 3380-3385 p.Conference paper (Refereed)
Abstract [en]

This paper presents results from an evaluation of three previously presented methods for segmentation of cell nuclei in lung cytology samples scanned by whole-slide scanners. Whole-slide images from seven cases of endobronchial ultrasound-guided transbronchial needle aspiration samples were used for extracting a number of regions of interest, in which approximately 2700 cell nuclei were manually segmented to form the ground truth. The segmented cells included benign bronchial epithelium, lymphocytes, granulocytes, histiocytes and malignant epithelial cells. The best results were obtained with a method based upon adaptive thresholding and an added step of clustering for distinguishing between cytoplasm and cell nuclei. This method achieved a mean DICE-score of 0.81 and a sensitivity and specificity of 0.88 and 0.81 respectively. In addition, this method was by far the fastest method, with a mean processing time of 7.8 s per image (2048 x 2048 pixels per image). By further improvements, such as lowering the false positive rate and using parallel computing hardware, this method has the potential to form the first building block in a system for computerized screening of whole-slide images in lung cytology.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 3380-3385 p.
Series
, International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-121334DOI: 10.1109/ICPR.2014.582ISI: 000359818003086ISBN: 978-1-4799-5208-3OAI: oai:DiVA.org:liu-121334DiVA: diva2:853445
Conference
22nd International Conference on Pattern Recognition (ICPR, Stockholm, Sweden, 24-28 Aug. 2014
Available from: 2015-09-14 Created: 2015-09-14 Last updated: 2015-10-01Bibliographically approved

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Forsberg, DanielMonsef, Nastaran
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Media and Information TechnologyThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)Department of Clinical Pathology and Clinical GeneticsDepartment of Science and Technology
Electrical Engineering, Electronic Engineering, Information Engineering

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ReferencesLink to record
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