Publications

Manuel Möller, Patrick Ernst, Daniel Sonntag and Andreas Dengel: “Automatic Spatial Plausibility Checks for Medical Object Recognition Results Using a Spatio-Anatomical Ontology”, Proc. of the International Conference on Knowledge Discovery and Information Retrieval (KDIR 2010), 25 – 28 October 2010, Valencia, Spain [BibTex]

Abstract:We present an approach to use medical expert knowledge represented in formal ontologies to check the results of automatic medical object recognition algorithms for spatial plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology which we extend with spatial relations between a number of anatomical entities. These relations are learned inductively from an annotated corpus of 3D volume data sets. The induction process is split into two parts: First, we generate a quantitative anatomical atlas using fuzzy sets to represent inherent imprecision. From this atlas we abstract onto a purely symbolic level to generate a generic qualitative model of the spatial relations in human anatomy. In our evaluation we describe how this model can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for spatial plausibility. Our results show that the combination of medical domain knowledge in formal ontologies and sub-symbolic object recognition yields improved overall recognition precision.

Manuel Möller, Patrick Ernst, Michael Sintek, Ralf Biedert, Andreas Dengel and Daniel Sonntag: “Representing the International Classification of Diseases Version 10 in OWL”, Proc. of the International Conference on Knowledge Engineering and Ontology Development (KEOD), 25 – 28 October 2010, Valencia, Spain [BibTex]

Abstract: Current efforts in the biomedical ontology community focus on establishing interoperability and data integration. In covering human diseases, one of the major international standards in clinical practice is the International Classification for Diseases (ICD), maintained by the World Health Organization (WHO). Several country- and language-specific adaptations exist which share the general structure of the WHO version but differ in certain details. This complicates the exchange of patient records and hampers data integration across language borders. We present our approach for modeling the hierarchy of the ICD-10 using the Web Ontology Language (OWL). Our model captures the hierarchical information of the ICD-10 as well as comprehensive class labels for English and German. Specialties such as “Exclusion” statements, which make statements about the disjointness of certain ICD-10 categories, are modeled in a formal way. For properties which exceed the expressivity of OWL-DL, we provide a separate OWL-Full component which allows us to use the hierarchical knowledge and class labels with existing OWL-DL reasoners and capture the additional information in a machine-interpretable way.

Manuel Möller, Patrick Ernst, Andreas Dengel: “Spatial Reasoning for Plausibility Checks of Medical Object Recognition Results Using the Foundational Model of Anatomy“, to appear in Proceedings of the 2nd Malaysian Joint Conference on Artificial Intelligence 2010 (MJCAI 2010), Kuching, Sarawak, Malaysia, 26th – 30th July 2010 [BibTex]

Abstract: We present a rule-based system using medical expert knowledge represented in a formal ontology to check the results of automatic medical object recognition algorithms for anatomical plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology and uses a set of forward rules executed by a Prolog engine. In our evaluation we describe how this approach can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for anatomical plausibility. Our results show that the combination of sub-symbolic object recognition, medical domain knowledge represented in formal ontologies and yields an improved overall recognition precision.

Daniel Sonntag and Manuel Möller: “Prototyping Semantic Dialogue Systems for Radiologists”, Proc. of the 6th International Conference on Intelligent Environments (IE’2010), Monash University, Kuala Lumpur, Malaysia, 2010 [BibTex]

Abstract: We present a semantic dialogue  system  for radiologists. In the future, speech-based semantic image retrieval and  annotation  of medical  images  should  provide  the  basis  for help  in  clinical  decision  support  and  computer  aided  diagnosis. We  will  describe  today’s  clinical  workflow  and  interaction requirements and focus on the design and implementation of our prototype system for patient  image search and  image annotation while  using  a  speech-based  dialogue  shell  in  the  radiology environment.  Ontology modeling  provides  the  backbone  for knowledge  representation in  the  dialogue  shell  and  the  specific medical application domain.

Manuel Möller, Patrick Ernst, Michael Sintek, Sascha Seifert, Gunnar Grimnes, Alexander Cavallaro, Andreas Dengel: “Combining Patient Metadata Extraction and Automatic Image Parsing for the Generation of an Anatomic Atlas”, in Proc. of the 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2010), Cardiff, UK, 8-10 September 2010 [BibTex]

Abstract: We present a system that integrates ontology-based metadata extraction from medical images with a state-of-the-art object recognition algorithm for 3D volume data sets generated by Computed Tomography scanners. Extracted metadata and automatically generated medical image annotations are stored as instances of OWL classes. This system is applied to a corpus of over 750 GB of clinical image data. A spatial database is used to store and retrieve 3D representations of the generated medical image annotations. Our integrated data representation allows to easily analyze our corpus and to estimate the quality of image metadata. A rule-based system is used to check the plausibility of the output of the automatic object recognition technique against the Foundational Model of Anatomy ontology. All combined, these methods are used to determine an appropriate set of metadata and image features for the automatic generation of a spatial atlas of human anatomy.

Ralf Biedert, Georg Buscher, Sven Schwarz, Manuel Möller, Andreas Dengel, Thomas Lottermann: “The Text 2.0 Framework” to appear in Proc. of the International Workshop on Eye Gaze in Intelligent Human Machine Interaction at the International Conference on Intelligent User Interfaces (IUI2010), Hong Kong, China,  7-10 February 2010 [BibTex]

Abstract: We created a simple-to-use framework to construct gaze-responsive applications using web technology focussing on text. A plugin enables any compatible browser to interpret a new set of gaze handlers that behave similar to existing HTML and JavaScript mouse and keyboard event facilities. Keywords like onFixation, onGazeOver, and onRead can be attached to parts of the DOMtree and are triggered on the corresponding viewing behavior. The plugin is part of a distributed architecture featuring a remote gaze provider and a number of assisting services and tools. Using this framework we implemented a number of applications providing help on comprehension difficulties.

Daniel Sonntag, Martin Huber, Manuel Möller, Alassane Ndiaye, Sonja Zillner, Alexander Cavallaro: “Design and Implementation of a Semantic Dialogue System for Radiologists”, book chapter in “Semantic Web: Standards, Tools and Ontologies”, Nova Science Publishers, Inc., Hauppauge, NY, USA, 2010, ISBN: 978-1-61668-540-9 [BibTex], [Link]

Abstract: This chapter describes a semantic dialogue system for radiologists in a comprehensive case study within the large-scale MEDICO project. MEDICO addresses the need for advanced semantic technologies in the search for medical image and patient data. The objectives are, first, to enable a seamless integration of medical images and different user applications by providing direct access to image semantics, and second, to design and implement a multimodal dialogue shell for the radiologist. Speech-based semantic image retrieval and annotation of medical images should provide the basis for help in clinical decision support and computer aided diagnosis.

We will describe the clinical workflow and interaction requirements and focus on the design and implementation of a multimodal user interface for patient/image search or annotation and its implementation while using a speech-based dialogue shell.  Ontology modeling provides the backbone for knowledge representation in the dialogue shell and the specific medical application domain; ontology structures are the communication basis of our combined semantic search and retrieval architecture which includes the MEDICO server, the triple store, the semantic search API, the medical visualization toolkit MITK, and the speech-based dialogue shell, amongst others. We will focus on usability aspects of multimodal applications, our storyboard and the implemented speech and touchscreen interaction design.

Daniel Sonntag, Manuel Möller: “A Multimodal Dialogue Mashup for Medical Image Semantics”, Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI 2010), Hong Kong, China, 7.-10. Februar 2010, pages 381-384, ACM, New York, NY, USA, ISBN: 978-1-60558-515-4, [BibTex], [PDF via ACM]

Abstract: This paper presents a multimodal dialogue mashup where different users are involved in the use of different user interfaces for the annotation and retrieval of medical images. Our solution is a mashup that integrates a multimodal interface for speech-based annotation of medical images and dialogue-based image retrieval with a semantic image annotation tool for manual annotations on a desktop computer. A remote RDF repository connects the annotation and querying task into a common framework and serves as the semantic backend system for the advanced multimodal dialogue a radiologist can use.

Sascha Seifert, Michael Kelm, Manuel Möller, Saikat Mukherjee, Alexander Cavallaro, Martin Huber, Dorin Comaniciu: “Semantic Annotation of Medical Images”, to appear in Proc. of SPIE Medical Imaging, San Diego, 13 – 18 February 2010 [BibTex]

Abstract: Diagnosis and treatment planning for patients can be signicantly improved by comparing with clinical images of other patients with similar anatomical and pathological characteristics. This requires the images to be annotated using common vocabulary from clinical ontologies. Current approaches to such annotation are typically manual, consuming extensive clinician time, and cannot be scaled to large amounts of imaging data in hospitals. On the other hand, automated image analysis while being very scalable do not leverage standardized semantics and thus cannot be used across specic applications. In our work, we describe an automated and context-sensitive work based on an image parsing system complemented by an ontology-based context-sensitive annotation tool. An unique characteristic of our framework is that it brings together the diverse paradigms of machine learning based image analysis and ontology based modeling for accurate and scalable semantic image annotation.

Daniel Sonntag, Manuel Möller: “Unifying Semantic Annotation and Querying in Biomedical Image Repositories”, to appear in Proc. of the International Conference on Knowledge Management and Information Sharing (KMIS), Madeira, Portugal, 6 – 8 October 2009 [BibTex]

Abstract: In the medical domain, semantic image retrieval can provide the basis for a new generation of sophisticated decision support and computer aided diagnosis systems. However, the acquisition of the necessary medical knowledge about the image contents poses new problems. We present a set of techniques for annotating images and querying image data sets, based on image semantics.  The unification of semantic annotation (using a GUI) and querying (using natural dialogue) in biomedical image repositories is based on a unified view on the knowledge acquisition process. At the core, this system uses central RDF repository to capture both medical domain knowledge as well as image annotations. We understand medical knowledge engineering as an interactive process between the knowledge engineer and the clinician. Our system supports the knowledge engineering in an interative process between the dialogue engineer and the clinician.