A Spatio-anatomical Medical Ontology and Automatic Plausibility Checks

Möller, Manuel; Sonntag, Daniel; Erñst, Patrick

doi:10.1007/978-3-642-29764-9_3

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…The first stage of this process abstracted relational information using a fuzzy set representation formalism. In the second stage we further abstracted from the fuzzy anatomical atlas to a symbolic level using an extension of the spatial relation model of the FMA (details can be found in Möller et al 2011). This approach (Möller et al 2011) augments medical domain ontologies and allows for an automatic detection of anatomically implausible constellations in the results of a state-of-the-art system for automatic object recognition in 3D CT scans.…”

Section: Spatial-anatomical Reasoningmentioning

confidence: 99%

“…In the second stage we further abstracted from the fuzzy anatomical atlas to a symbolic level using an extension of the spatial relation model of the FMA (details can be found in Möller et al 2011). This approach (Möller et al 2011) augments medical domain ontologies and allows for an automatic detection of anatomically implausible constellations in the results of a state-of-the-art system for automatic object recognition in 3D CT scans. The output of this preprocessing step is a feedback on which anatomical entities are most likely to have been located incorrectly (thereby, the necessary spatio-anatomical knowledge is learned from a large corpus of annotated medical image volume data sets).…”

Section: Spatial-anatomical Reasoningmentioning

confidence: 99%

“…We performed a systematic evaluation of the positions on our manually labeled corpus (anatomy detections) using four-fold cross evaluation. In total, 1,119 detector results have been inspected and classified manually: there are 388 sufficiently correct detector results and 147 perfect detector results (Möller et al 2011). Our results show that the average difference in percent between the spatial relation instances in the learned model and the instances generated for an element from the evaluation set is an appropriate measure for the spatial positions (showing that in our case, lymphatic occurrences are either only above, only below, or on both sides of the diaphragm).…”

Section: Lymphnode Occurrence Xy9 Is Below: Diaphragmmentioning

confidence: 99%

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Image metadata reasoning for improved clinical decision support

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2012

Netw Model Anal Health Inform Bioinforma

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Today, clinicians rely more and more on medical images for screening, diagnosis, treatment planning, and follow-up examinations. While medical images provide a wealth of information for clinicians, content information cannot be automatically integrated into advanced medical applications such as those for the clinical decision support. The implementation of advanced medical applications requires means for the automated post-processing of medical image annotations. In this article we describe how we made use of reasoning technologies to post-process medical image annotations in the context of the automated staging process of lymphoma patients. First, we describe how automatic anatomy detectors and OWL reasoning processes can be used to preprocess medical images automatically and in a way that makes accurate input to further, more complex reasoning processes possible. Second, we enhance and integrate patients' image metadata by formalized practical clinical knowledge sources. The resulting combined data serve as input to an automatic reasoning process in order to stage lymphoma patients automatically.

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Section: Spatial-anatomical Reasoningmentioning

confidence: 99%

Section: Spatial-anatomical Reasoningmentioning

confidence: 99%

Section: Lymphnode Occurrence Xy9 Is Below: Diaphragmmentioning

confidence: 99%

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Image metadata reasoning for improved clinical decision support

Zillner¹,

Sonntag²

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Netw Model Anal Health Inform Bioinforma

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Automatic Classification of Cancer Tumors Using Image Annotations and Ontologies

Luque

Rubin

Moreira

2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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Abstract-Information about cancer stage in a patient is crucial when clinicians assess treatment progress. Determining cancer stage is a process that takes into account the description, location, characteristics and possible metastasis of cancerous tumors in a patient. It should follow classification standards, such as TNM Classification of Malignant Tumors. However, in clinical practice, the implementation of this process can be tedious and error-prone and create uncertainty. In order to alleviate these problems, we intend to assist radiologists by providing a second opinion in the evaluation of cancer stage in patients. For doing this, Semantic Web technologies, such as ontologies and reasoning, will be used to automatically classify cancer stages. This classification will use semantic annotations, made by radiologists (using the ePAD tool) and stored in the AIM format, and rules of an ontology representing the TNM standard. The whole process will be validated through a proof of concept with users from the Radiology Dept. of the Stanford University.

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A Spatio-anatomical Medical Ontology and Automatic Plausibility Checks

Cited by 2 publications

References 14 publications

Image metadata reasoning for improved clinical decision support

Image metadata reasoning for improved clinical decision support

Automatic Classification of Cancer Tumors Using Image Annotations and Ontologies

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