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Objective automated quantification of fluorescence signal in histological sections of rat lens
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ophthalmology.ORCID iD: 0000-0001-7325-7358
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ophthalmology.ORCID iD: 0000-0003-0654-5856
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ophthalmology.
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2017 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91, no 8, p. 815-821Article in journal (Refereed) Published
Abstract [en]

Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab™. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n = 395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers.

Place, publisher, year, edition, pages
2017. Vol. 91, no 8, p. 815-821
National Category
Ophthalmology Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-322627DOI: 10.1002/cyto.a.23131ISI: 000408333700011PubMedID: 28494118OAI: oai:DiVA.org:uu-322627DiVA, id: diva2:1098937
Available from: 2017-05-11 Created: 2017-05-28 Last updated: 2018-09-03Bibliographically approved

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Talebizadeh, NooshinYu, ZhaohuaKronschläger, MartinSöderberg, PerWählby, Carolina

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OphthalmologyDivision of Visual Information and InteractionScience for Life Laboratory, SciLifeLabComputerized Image Analysis and Human-Computer Interaction
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Cytometry Part A
OphthalmologyMedical Image Processing

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