Literature DB >> 23573905

Improving efficiency in stereology: a study applying the proportionator and the autodisector on virtual slides.

K K Keller1, I T Andersen, J B Andersen, U Hahn, K Stengaard-Pedersen, E-M Hauge, J R Nyengaard.   

Abstract

Cell counting in stereology is time-consuming. The proportionator is a new stereological sampling method combining automatic image analysis and non-uniform sampling. The autodisector on virtual slides combines automatic generation of disector pairs with the use of digital images. The aim of the study was to investigate the time efficiency of the proportionator and the autodisector on virtual slides compared with traditional methods in a practical application, namely the estimation of osteoclast numbers in paws from mice with experimental arthritis and control mice. Tissue slides were scanned in a digital slide scanner and the autodisector was applied on the obtained virtual tissue slides. Every slide was partitioned into fields of view, and cells were counted in all of them. Based on the original exhaustive data set comprising 100% of fields of view and covering the total section area, a proportionator sampling and a systematic, uniform random sampling were simulated. We found that the proportionator was 50% to 90% more time efficient than systematic, uniform random sampling. The time efficiency of the autodisector on virtual slides was 60% to 100% better than the disector on tissue slides. We conclude that both the proportionator and the autodisector on virtual slides may improve efficiency of cell counting in stereology.
© 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Entities:  

Mesh:

Year:  2013        PMID: 23573905     DOI: 10.1111/jmi.12044

Source DB:  PubMed          Journal:  J Microsc        ISSN: 0022-2720            Impact factor:   1.758


  8 in total

Review 1.  Basic quantitative morphological methods applied to the central nervous system.

Authors:  Lutz Slomianka
Journal:  J Comp Neurol       Date:  2020-08-01       Impact factor: 3.215

2.  Application of the Physical Disector Principle for Quantification of Dopaminergic Neuronal Loss in a Rat 6-Hydroxydopamine Nigral Lesion Model of Parkinson's Disease.

Authors:  Katrine Fabricius; Pernille Barkholt; Jacob Jelsing; Henrik H Hansen
Journal:  Front Neuroanat       Date:  2017-12-08       Impact factor: 3.856

3.  Deep Learning With Sampling in Colon Cancer Histology.

Authors:  Mary Shapcott; Katherine J Hewitt; Nasir Rajpoot
Journal:  Front Bioeng Biotechnol       Date:  2019-03-27

4.  Cell counting in human endobronchial biopsies--disagreement of 2D versus 3D morphometry.

Authors:  Vlad A Bratu; Veit J Erpenbeck; Antonia Fehrenbach; Tanja Rausch; Susanne Rittinghausen; Norbert Krug; Jens M Hohlfeld; Heinz Fehrenbach
Journal:  PLoS One       Date:  2014-03-24       Impact factor: 3.240

Review 5.  Assessing particle and fiber toxicology in the respiratory system: the stereology toolbox.

Authors:  Christina Brandenberger; Matthias Ochs; Christian Mühlfeld
Journal:  Part Fibre Toxicol       Date:  2015-10-31       Impact factor: 9.400

6.  The Automatic Proportionator Estimator Is Highly Efficient for Estimation of Total Number of Sparse Cell Populations.

Authors:  Rogely W Boyce; Hans J G Gundersen
Journal:  Front Neuroanat       Date:  2018-03-21       Impact factor: 3.856

7.  Global Trends in Application of Stereology as a Quantitative Tool in Biomedical Research.

Authors:  Maulilio J Kipanyula; Alfred S Sife
Journal:  Biomed Res Int       Date:  2018-09-13       Impact factor: 3.411

8.  Implementation of deep neural networks to count dopamine neurons in substantia nigra.

Authors:  Anna-Maija Penttinen; Ilmari Parkkinen; Sami Blom; Jaakko Kopra; Jaan-Olle Andressoo; Kari Pitkänen; Merja H Voutilainen; Mart Saarma; Mikko Airavaara
Journal:  Eur J Neurosci       Date:  2018-09-20       Impact factor: 3.386

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.