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Computerized Cell and Tissue Analysis
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.
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The latest advances in digital cameras combined with powerful computer software enable us to store high-quality microscopy images of specimen. Studying hundreds of images manually is very time consuming and has the problem of human subjectivity and inconsistency. Quantitative image analysis is an emerging field and has found its way into analysis of microscopy images for clinical and research purposes. When developing a pipeline, it is important that its components are simple enough to be generalized and have predictive value. This thesis addresses the automation of quantitative analysis of tissue in two different fields: pathology and plant biology.

Testicular tissue is a complex structure consisting of seminiferous tubules. The epithelial layer of a seminiferous tubule contains cells that differentiate from primitive germ cells to spermatozoa in a number of steps. These steps are combined in 12 stages in the cycle of the seminiferous epithelium in the mink. The society of toxicological pathology recommends classifying the testicular epithelial into different stages when assessing tissue damage to determine if the dynamics in the spermatogenic cycle have been disturbed. This thesis presents two automated methods for fast and robust segmentation of tubules, and an automated method of staging them. For better accuracy and statistical analysis, we proposed to pool stages into 5 groups. This pooling is suggested based on the morphology of tubules. In the 5 stage case, the overall number of correctly classified tubules is 79.6%.

Contextual information on the localization of fluorescence in microscopy images of plant specimen help us to better understand differentiation and maturation of stem cells into tissues. We propose a pipeline for automated segmentation and classification of the cells in a whole cross-section of Arabidopsis hypocotyl, stem, or root. As proof-of-concept that the classification provides a meaningful basis to group cells for fluorescence characterization, we probed tissues with an antibody specific to xylem vessels in the secondary cell wall. Fluorescence intensity in different classes of cells is measured by the pipeline. The measurement results clearly show that the xylem vessels are the dominant cell type that exhibit a fluorescence signal.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. , 63 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1262
Keyword [en]
Image processing, Cell, Tissue, Segmentation, Classification, Histology
National Category
Medical Image Processing Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-252425ISBN: 978-91-554-9269-4 (print)OAI: oai:DiVA.org:uu-252425DiVA: diva2:810263
Public defence
2015-06-12, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2015-06-03 Created: 2015-05-06 Last updated: 2015-07-07
List of papers
1. Analyzing Tubular Tissue in Histopathological Thin Sections
Open this publication in new window or tab >>Analyzing Tubular Tissue in Histopathological Thin Sections
2012 (English)In: 2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA), IEEE conference proceedings, 2012, 1-6 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE conference proceedings, 2012
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-188548 (URN)10.1109/DICTA.2012.6411735 (DOI)000316318400071 ()
Conference
International Conference on Digital Image Computing Techniques and Applications (DICTA), 3-5 Dec, 2012, Fremantle, AUSTRALIA
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2015-06-03Bibliographically approved
2. Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut
Open this publication in new window or tab >>Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut
2013 (English)In: Image Analysis and Processing – ICIAP 2013: Part II, 2013, 201-208 p.Conference paper, Published paper (Refereed)
Abstract [en]

Computerized image processing has provided us with valuable tools for analyzing histology images. However, histology images are complex, and the algorithm which is developed for a data set may not work for a new and unseen data set. The preparation procedure of the tissue before imaging can significantly affect the resulting image. Even for the same staining method, factors like delayed fixation may alter the image quality. In this paper we face the challenging problem of designing a method that works on data sets with strongly varying quality. In environmental research, due to the distance between the site where the wild animals are caught and the laboratory, there is always a delay in fixation. Here we suggest a segmentation method based on the structural information of epithelium cell layer in testicular tissue. The cell nuclei are detected using the fast radial symmetry filter. A graph is constructed on top of the epithelial cells. Graph-cut optimization method is used to cut the links between cells of different tubules. The algorithm is tested on five different groups of animals. Group one is fixed immediately, three groups were left at room temperature for 18, 30 and 42 hours respectively, before fixation. Group five was frozen after 6 hours in room temperature and thawed. The suggested algorithm gives promising results for the whole data set.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8157
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-210299 (URN)10.1007/978-3-642-41184-7_21 (DOI)000329811200021 ()978-3-642-41183-0 (ISBN)978-3-642-41184-7 (ISBN)
Conference
17th International Conference on Image Analysis and Processing (ICIAP), Naples, Italy, September 9-13, 2013
Available from: 2013-11-05 Created: 2013-11-05 Last updated: 2015-06-03Bibliographically approved
3. Computerized Study of Developmental Stages in Mink Testicular Tissue
Open this publication in new window or tab >>Computerized Study of Developmental Stages in Mink Testicular Tissue
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(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-252411 (URN)
Available from: 2015-05-06 Created: 2015-05-06 Last updated: 2015-06-03
4. Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
Open this publication in new window or tab >>Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
2016 (English)In: Frontiers in Plant Science, ISSN 1664-462X, E-ISSN 1664-462X, Vol. 7, 119Article in journal (Refereed) Published
Abstract [en]

While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to (1) segment radial plant organs into individual cells, (2) classify cells into cell type categories based upon Random Forest classification, (3) divide each cell into sub-regions, and (4) quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

Keyword
automated image analysis; confocal microscopy; Arabidopsis; hypocotyl; automated phenotyping; code:matlab
National Category
Plant Biotechnology
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-252412 (URN)10.3389/fpls.2016.00119 (DOI)000369802700001 ()
Funder
Bio4EnergyVINNOVA
Available from: 2016-02-09 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved
5. Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research
Open this publication in new window or tab >>Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research
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2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 5, e0125139Article in journal (Refereed) Published
National Category
Veterinary Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-252410 (URN)10.1371/journal.pone.0125139 (DOI)000353887100081 ()25933113 (PubMedID)
Available from: 2015-05-01 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved

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Fakhrzadeh, Azadeh

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