Stammdaten

Titel: GLENDA: Gynecologic Laparoscopy Endometriosis Dataset
Untertitel:
Kurzfassung:

Gynecologic laparoscopy as a type of minimally invasive surgery

(MIS) is performed via a live feed of a patient's abdomen surveying

the insertion and handling of various instruments for conducting

treatment. Adopting this kind of surgical intervention not only facilitates

a great variety of treatments, the possibility of recording said video

streams is as well essential for numerous post-surgical activities, such

as treatment planning, case documentation and education. Nonetheless,

the process of manually analyzing surgical recordings, as it is carried out

in current practice, usually proves tediously time-consuming. In order

to improve upon this situation, more sophisticated computer vision as

well as machine learning approaches are actively developed. Since most

of such approaches heavily rely on sample data, which especially in the

medical eld is only sparsely available, with this work we publish the

Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) { an image

dataset containing region-based annotations of a common medical

condition named endometriosis, i.e. the dislocation of uterine-like tissue.

The dataset is the rst of its kind and it has been created in collaboration

with leading medical experts in the eld.

Schlagworte: lesion detection, endometriosis localization, medical dataset, region-based annotations, gynecologic laparoscopy
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Proceedings of the 26th International Conference in MultiMedia Modeling (MMM 2020) (Part II)
Proceedings of the 26th International Conference in MultiMedia Modeling (MMM 2020) (Part II)
zur Publikation
 ( Springer; W. Cheng, J. Kim, W. Chu, P. Cui, J. Choi, M. Hu, W. De Neve )
Erscheinungsdatum: 24.12.2019
Titel der Serie: Lecture Notes in Computer Science
Bandnummer: 11962
Erstveröffentlichung: Ja
Version: -
Seite: S. 439 - 450

Identifikatoren

ISBN:
  • 978-3-030-37733-5
  • 978-3-030-37734-2
ISSN: -
DOI: http://dx.doi.org/10.1007/978-3-030-37734-2_36
AC-Nummer: -
Homepage: https://www.researchgate.net/publication/338189084_GLENDA_Gynecologic_Laparoscopy_Endometriosis_Dataset/link/5e1c30554585159aa4cb7378/download
Open Access
  • Online verfügbar (nicht Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Österreich
   martina.steinbacher@uni-klu.ac.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 102020 - Medizinische Informatik (305905)
Forschungscluster Kein Forschungscluster ausgewählt
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: II)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Distributed Multimedia Systems

Kooperationen

Organisation Adresse
Ludwig-Maximilians-Universität
München
Deutschland
DE  München
Universität Ulm
Ulm
Deutschland
DE  Ulm

Beiträge der Publikation

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