Computerized Cell and Tissue Analysis
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1262
Image processing, Cell, Tissue, Segmentation, Classification, Histology
Medical Image Processing Computer and Information Science
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-252425ISBN: 978-91-554-9269-4OAI: oai:DiVA.org:uu-252425DiVA: diva2:810263
2015-06-12, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 09:15 (English)
Rajpoot, Nasir, Associate Professor
Borgefors, Gunilla, ProfessorLuengo Hendriks, Cris, DocentHolm, Lena, Professor
List of papers