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Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
2016 (Engelska)Ingår i: Frontiers in Plant Science, ISSN 1664-462X, E-ISSN 1664-462X, Vol. 7, artikel-id 119Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
2016. Vol. 7, artikel-id 119
Nyckelord [en]
automated image analysis; confocal microscopy; Arabidopsis; hypocotyl; automated phenotyping; code:matlab
Nationell ämneskategori
Växtbioteknologi
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-252412DOI: 10.3389/fpls.2016.00119ISI: 000369802700001OAI: oai:DiVA.org:uu-252412DiVA, id: diva2:810172
Forskningsfinansiär
Bio4EnergyVINNOVATillgänglig från: 2016-02-09 Skapad: 2015-05-06 Senast uppdaterad: 2017-12-04Bibliografiskt granskad
Ingår i avhandling
1. Computerized Cell and Tissue Analysis
Öppna denna publikation i ny flik eller fönster >>Computerized Cell and Tissue Analysis
2015 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2015. s. 63
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1262
Nyckelord
Image processing, Cell, Tissue, Segmentation, Classification, Histology
Nationell ämneskategori
Medicinsk bildbehandling Data- och informationsvetenskap
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-252425 (URN)978-91-554-9269-4 (ISBN)
Disputation
2015-06-12, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 09:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2015-06-03 Skapad: 2015-05-06 Senast uppdaterad: 2018-01-11

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Fakhrzadeh, AzadehLuengo Hendriks, Cris L.

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