title: Learning directional relative positions between mediastinal lymph node stations and organs.
abstract: 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.
  
  
  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).
  
  
  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.
  
  
  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.
authors.affiliation: Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044, INSA-Lyon; Université Lyon 1, F-69621 Lyon, France and Léon Bérard Cancer Center, Université de Lyon, F-69373 Lyon, France.
authors.forename: David
authors.initials: D
authors.lastname: Sarrut
authors.affiliation: Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044, INSA-Lyon; Université Lyon 1, F-69621 Lyon, France and Léon Bérard Cancer Center, Université de Lyon, F-69373 Lyon, France.
authors.forename: Simon
authors.initials: S
authors.lastname: Rit
authors.affiliation: Léon Bérard Cancer Center, Université de Lyon, F-69373 Lyon, France.
authors.forename: Line
authors.initials: L
authors.lastname: Claude
authors.affiliation: Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044, INSA-Lyon; Université Lyon 1, F-69621 Lyon, France and Léon Bérard Cancer Center, Université de Lyon, F-69373 Lyon, France.
authors.forename: Romulo
authors.initials: R
authors.lastname: Pinho
authors.affiliation: Department of Radiation Oncology, Andrew Love Cancer Centre, Barwon Health, Geelong 3220, Australia.
authors.forename: Graham
authors.initials: G
authors.lastname: Pitson
authors.affiliation: Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044, INSA-Lyon; Université Lyon 1, F-69621 Lyon, France and Léon Bérard Cancer Center, Université de Lyon, F-69373 Lyon, France.
authors.forename: Gauthier
authors.initials: G
authors.lastname: Bouilhol
authors.affiliation: Department of Radiation Oncology, Andrew Love Cancer Centre, Barwon Health, Geelong 3220, Australia.
authors.forename: Rod
authors.initials: R
authors.lastname: Lynch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D062485:Breath Holding; D016208:Databases, Factual; D005240:Feasibility Studies; D006801:Humans; D020407:Internet; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic / Q000379:methods*; D013909:Thorax; D014057:Tomography, X-Ray Computed / Q000379:methods*