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Charité - Medical University of Berlin, Germany
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2 Papers

A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images

Topics: Segmentation and Grouping; Machine Learning Technologies for Vision; Early and Biologically-Inspired Vision; Features Extraction; Medical Image Applications

Authors: Harshita Sharma 1 , Norman Zerbe 2 , Daniel Heim 2 , Stephan Wienert 3 , Hans-Michael Behrens 4 , Olaf Hellwich 1 and Peter Hufnagl 2

Affiliations: 1 Technische Universität Berlin, Germany , 2 Charité - Universitätsmedizin Berlin, Germany , 3 VMscope GmbH, Germany , 4 Charité - Universitätsmedizin Berlin and Christian-Albrechts University, Germany

Digital Object Identifier: 10.5220/0005247900370046

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-091-8

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This paper describes a multi-resolution technique to combine diagnostically important visual information at different magnifications in H&E whole slide images (WSI) of gastric cancer. The primary goal is to improve the results of nuclei segmentation method for heterogeneous histopathological datasets with variations in stain intensity and malignancy levels. A minimum-model nuclei segmentation method is first applied to tissue images at multiple resolutions, and a comparative evaluation is performed. A comprehensive set of 31 nuclei features based on color, texture and morphology are derived from the nuclei segments. AdaBoost classification method is used to classify these segments into a set of pre-defined classes. Two classification approaches are evaluated for this purpose. A relevance score is assigned to each class and a combined segmentation result is obtained consisting of objects with higher visual significance from individual magnifications, thereby preserving both coarse and fine details in the image. Quantitative and visual assessment of combination results shows that they contain comprehensive and diagnostically more relevant information than in constituent magnifications.
Collapse Button Related Ontology Subjects/Areas/Topics (7)
Applications and Services

Features Extraction

Medical Image Applications

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Segmentation and Grouping
Early and Biologically-Inspired Vision

Patient Identification for Clinical Trials with Ontology-based Information Extraction from Documents

Topics: Natural Language Processing; Applications and Case-Studies; Domain Analysis and Modeling; Enterprise Ontology

Authors: Peter Geibel 1 , Hebun Erdur 1 , Lothar Zimmermann 1 , Stefan Krüger 1 , Kati Jegzentis 2 , Josef Schepers 1 , Anne Becker 3 , Frank Müller 1 , Christian Hans Nolte 1 , Jan Friedrich Scheitz 1 , Serdar Tütüncü 1 , Tatiana Usnich 1 , Markus Frick 3 , Martin Trautwein 3 , Thorsten Schaaf 1 , Alfred Holzgreve 3 and Thomas Tolxdorff 1

Affiliations: 1 Charité - Universitätsmedizin Berlin, Germany , 2 Charité - Universitätsmedizin Berlin, Germany , 3 Vivantes - Netzwerk für Gesundheit GmbH, Germany

Digital Object Identifier: 10.5220/0004544702300236

Proceedings of KEOD 2013/IC3K, ISBN: 978-989-8565-81-5

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In this paper, we describe the use of ontologies in the context of a system for recruiting patients for clinical trials, which is currently being tested at the {\em Charit\'{e} – Universitätsmedizin Berlin}, one of the largest university hospitals in Europe. The main purpose of the CRDW (Clinical Research Data Warehouse) is to support patient recruitment for clinical trials based on routine data from the hospital's clinical information system (CIS). In contrast to most other systems for similar purposes, the CRDW also makes use of information that is present in clinical documents like admission reports, radiological findings, and discharge letters. The linguistic analysis recognizes negated and coordinated phrases. It is supported by clinical domain ontologies that enable the identification of main terms and their properties, as well as semantic search with synonyms, hypernyms, and syntactic variants. The focus of this paper is the description of our ontology model, which we tailored to the particular requirements of our application. In the article, we will also provide an evaluation of the system based on experimental data obtained from the daily routine work of the study assistants.
Collapse Button Related Ontology Subjects/Areas/Topics (12)
Applications

Domain Analysis and Modeling

Natural Language Processing

Applications and Case-studies

Enterprise Ontology

Ontologies and the Semantic Web

Artificial Intelligence

Knowledge Engineering and Ontology Development

Pattern Recognition

Data Engineering

Knowledge-Based Systems

Symbolic Systems

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