96. Jahrestagung der Deutschen Gesellschaft für Pathologie e. V ...

SA-P-104
Process oriented scientific data management in the Open European
Nephrology Science Center
T . Schrader 1 , S . Niepage 2 , C . Hahn 2 , S . Hanß 3
1 University of Applied Sciences Brandenburg, FB Informatics & Media,
Brandenburg, 2 Charité – Universitätsmedizin Berlin, Institut für Pathologie,
3 Charité – Universitätsmedizin Berlin, Institut für Medizinische Informatik
Aims. The Open European Nephrology Science Center (OpEN.SC) is a
center for research related clinical data supported by the German Research
Foundation. The reuse of clinical for translational medicine is
a cornerstone for the further scientific development. Various projects
try to collect data from different resources for scientific purpose. The
OpEN.SC platform consists of process oriented tools for data import,
management, analysis and presentation and solved the two main problems
in scientific data management: storage of data taking to consideration
the legal issues (secure patient data) and save the intellectual
properties of the diagnostic and therapeutic process by the physicians.
Methods. The platform based on a Service Oriented Architecture and
consists of various web services as modules. The orchestration of these
web services is realized by business process models. At the backend an
Apache Geronimo Application Server ensures the availability of the web
services. The Open Source database engine PostgreSQL is used to store
the data from three different resources. The user interface is realized
by OpenSource Liferay Portal. Different tools for retrieval and image
analysis are available. The processes were modeled using the standard
BPMN (Business Process Modelling Notation by OMG).
Results. The Open European Nephrology Science Center is a flexible
platform for scientific data management. Flexibility means that data
from different resources, with different structures and various file types
can be stored at this server and managed related to the resources.
Each resource has a complete control and transparency for its own data.
Due to the Service Oriented Architecture the platform is scalable. The
process oriented modelling offers the opportunity to adapt the system
for each specific use case and any type of data management model. The
process models can be used for business simulation to evaluate the impact
of changes very early.
Conclusions. Scientific data management should cover different aspects
of data handling: data import, storage, retrieval, access and presentation
concerning all legal issues and changing as well as increasing requirements
to storage, retrieval and analysis. Flexibility is a cornerstone
for management data from different resources and can be realized by
application of business process modeling, executing, analysis and simulation.
SA-P-105
CognitionMaster: an open source biomedical image analysis
development framework
S . Wienert1 , D . Heim1 , K . Saeger2 , C . Denkert1 , P . Hufnagl1 , F . Klauschen1 1 2 Charité University Hospital Berlin, Institute of Pathology, Berlin, VMscope
GmbH, Berlin
Aims. Automated microscopic image analysis has been a research focus
in medical informatics since many years. The topic has also attracted
attention among pathologists who face an increasing demand for a
standardized and quantitative evaluation of (immuno-)histological parameters
in patient specimens. Here, computerized image analysis may
assist pathologists and can also help to more efficiently test hypothesis
on the relevance of certain tissue properties. One limitation in this field
is the difficulty on one side for computer scientists to efficiently develop
and test image analysis algorithms with realistic histological data,
and on the other side for (quantitatively-inclined) pathologists to test
and optimize the potentially useful analysis software: While developers
would usually favour sophisticated software environments that are usually
inaccessible to non-computer scientists and do not facilitate easy
testing of realistic image data, pathologists are normally confronted
with ready-to-use software applications with no or limited flexibility.
Our aim was to design an open and flexible software platform that may
support collaboration between pathologists and computer scientists.
Methods. The software was implemented in C# for Microsoft .NET.
SharpDevelop was used for the integrated editing of C# code. Icons
from the Tango! project were use for the graphical user interface.
Results. We present an open-source software platform that may be used
for a broad spectrum of tasks in the field of medical image analysis:
Ranging from algorithm development with an integrated C# compiler
to one-click analysis provided through a powerful plug-in interface. A
novel object layer structure was designed to handle image objects and
their properties and therefore allow high-level formulations of image
analysis algorithms. The tool provides flexible and interactive functionality
with a variety of image analysis algorithms that may be combined
in process chains, an object model editor, and plotting/statistics functions.
Conclusions. The platform presented here may help both computer
scientists and pathologists to efficiently design and test novel image
analysis approaches and quickly obtain a “first guess” on their practical
utility. It may therefore foster collaboration in the field of quantitative
virtual microscopy and accelerate the integration of novel image analysis
approaches into diagnostic applications.
SA-P-106
Computer-assisted histology for the diagnosis of Barrett’s
Esophagus – a pilot study
C . Dach1 , C . Geppert2 , S . Friedl1 , M . Benz1 , C . Münzenmayer1 , A . Hartmann2 ,
M . Vieth3 , T . Wittenberg1 1 2 Fraunhofer IIS, Erlangen, University Erlangen, Institute for Pathology,
Erlangen, 3Klinikum Bayreuth, Institute for Pathology, Bayreuth
Aims. Gastroesophageal reflux disease (GERD) is one of the most common
and in the frequency increasing diseases in the western world. Intestinal
metaplasia, or “Barrett’s Esophagus”, is a precancerous condition
and complication of GERD. A large interobserver variation is known
in histopathology of Barrett’s Esophagus. Hence, it makes sense to support
pathologists with an automated pre-analysis of the images. Goal of
this study is the evaluation of possibilities to differentiate three types of
tissue automatically, namely Barrett’s Esophagus (BE), normal cardiac
mucosa (CA) and normal squamous epithelium of the esophagus (EP).
Methods. Histological slides of 86 randomly selected patients with Barrett’s
Esophagus have been digitized with a high-resolution whole-slide
scanner (3DHistech). 26 data sets were selected in which equally all 3 types
of tissue (BE, CA, EP) are depicted. In these data sets 50 rectangular
regions for each of the 3 classes were manually labeled. It was evaluated,
which type of image-based features, namely color enhanced 2nd order
texture statistics and statistical-geometrical features, can be used for a
best differentiation of the 3 tissue classes. Furthermore, various parameters
for the texture features were evaluated. For the classification step
a nearest-neighbor classifier with various parameters was applied. For
each experiment all classification rates as well as the confusion tables
were computed.
Results. Using an n-fold cross validation and a combination of 2nd orders
statistical features, a maximum diagnostic classification rate of
90% (BE 88%, CA 84%, EP 100%) could be achieved, denoting the possibility
of a correct differentiation of the diagnostic classes with respect to
the annotated ground truth. The highest possible classification rate for
the discrimination of Barrett’s Esophagus was achieved with 90% on a
basis of color co-occurrence matrices and correlates to a total classification
rate of 89% over all 3 classes (CA 76%, EP 100%).
Conclusions. The results show, that the evaluated classes (BE, CA, EP)
can be differentiated quite well on this image data base by applying color-extended
texture features, whereas the detection and elimination
of EP tissue is possible with a high sensitivity. Hence, the basic criteria
Der Pathologe · Supplement 1 · 2012 |
171