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Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas
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.
Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA, USA and Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
Dept. of Electronics and Communication Engineering (ECE), National Institute of Technology (NIT), Tiruchirappalli, India.
Vise andre og tillknytning
2013 (engelsk)Inngår i: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 4, nr 14Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background:

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

Materials and Methods:

The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

Results:

We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

Conclusions:

Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

sted, utgiver, år, opplag, sider
2013. Vol. 4, nr 14
HSV kategori
Forskningsprogram
Medicinsk beteendevetenskap; Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-212564DOI: 10.4103/2153-3539.109881OAI: oai:DiVA.org:uu-212564DiVA, id: diva2:678330
Tilgjengelig fra: 2013-12-11 Laget: 2013-12-11 Sist oppdatert: 2017-12-06bibliografisk kontrollert
Inngår i avhandling
1. Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
Åpne denne publikasjonen i ny fane eller vindu >>Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2014. s. 129
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1128
Emneord
image analysis, breast cancer diagnosis, digital histopathology, immunohistochemistry, biomarker quantification
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-219306 (URN)978-91-554-8889-5 (ISBN)
Disputas
2014-04-11, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-03-20 Laget: 2014-02-26 Sist oppdatert: 2014-07-21

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Forlagets fullteksthttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3678740

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