Serveur d'exploration sur les relations entre la France et l'Australie

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Learning directional relative positions between mediastinal lymph node stations and organs.

Identifieur interne : 003623 ( PubMed/Curation ); précédent : 003622; suivant : 003624

Learning directional relative positions between mediastinal lymph node stations and organs.

Auteurs : David Sarrut [France] ; Simon Rit [France] ; Line Claude [France] ; Romulo Pinho [France] ; Graham Pitson [Australie] ; Gauthier Bouilhol [France] ; Rod Lynch [Australie]

Source :

RBID : pubmed:24877816

Descripteurs français

English descriptors

Abstract

To automatically learn directional relative positions (DRP) between mediastinal lymph node stations and anatomical organs. Those spatial relationships are used to semiautomatically segment the stations in thoracic CT images.

DOI: 10.1118/1.4873677
PubMed: 24877816

Links toward previous steps (curation, corpus...)


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pubmed:24877816

Le document en format XML

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<term>Feasibility Studies</term>
<term>Humans</term>
<term>Internet</term>
<term>Lung Neoplasms (diagnostic imaging)</term>
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<div type="abstract" xml:lang="en">To automatically learn directional relative positions (DRP) between mediastinal lymph node stations and anatomical organs. Those spatial relationships are used to semiautomatically segment the stations in thoracic CT images.</div>
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<AbstractText Label="PURPOSE" NlmCategory="OBJECTIVE">To automatically learn directional relative positions (DRP) between mediastinal lymph node stations and anatomical organs. Those spatial relationships are used to semiautomatically segment the stations in thoracic CT images.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">Fuzzy maps of DRP were automatically extracted by a learning procedure from a database composed of images with stations and anatomical structures manually segmented by consensus between experts. Spatial relationships common to all patients were retained. The segmentation of a new image used an initial rough delineation of anatomical organs and applied the DRP operators. The algorithm was tested with a leave-one-out approach on a database of 5 patients with 10 lymph stations and 30 anatomical structures each. Results were compared to expert delineations with dice similarity coefficient (DSC) and bidirectional local distance (BLD).</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">The overall mean DSC was 66% and the mean BLD was 1.7 mm. Best matches were obtained from stations S3P or S4R while lower matches were obtained for stations 1R and 1L. On average, more than 30 spatial relationships were automatically extracted for each station.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">This feasibility study suggests that mediastinal lymph node stations could be satisfactory segmented from thoracic CT using automatically extracted positional relationships with anatomical organs. This approach requires the anatomical structures to be initially roughly delineated. A similar approach could be applied to other sites where spatial relationships exists between anatomical structures. The complete database of the five reference cases is made publicly available.</AbstractText>
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