Landesberger von Antburg, Tatiana ; Bremm, Sebastian ; Kirschner, Matthias ; Wesarg, Stefan ; Kuijper, Arjan (2013)
Visual Analytics for Model-based Medical Image Segmentation: Opportunities and Challenges.
In: Expert Systems with Applications, 40 (12)
doi: 10.1016/j.eswa.2013.03.006
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Segmentation of medical images is a prerequisite in clinical practice. Many segmentation algorithms use statistical shape models. Due to the lack of tools providing prior information on the data, standard models are frequently used. However, they do not necessarily describe the data in an optimal way. Model-based segmentation can be supported by Visual Analytics tools, which give the user a deeper insight into the correspondence between data and model result. Combining both approaches, better models for segmentation of organs in medical images are created. In this work, we identify the main tasks and problems in model-based image segmentation. As a proof of concept, we show that already small visual-interactive extensions can be very beneficial. Based on these results, we present research challenges for Visual Analytics in this area.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Landesberger von Antburg, Tatiana ; Bremm, Sebastian ; Kirschner, Matthias ; Wesarg, Stefan ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual Analytics for Model-based Medical Image Segmentation: Opportunities and Challenges |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Expert Systems with Applications |
| Jahrgang/Volume einer Zeitschrift: | 40 |
| (Heft-)Nummer: | 12 |
| DOI: | 10.1016/j.eswa.2013.03.006 |
| Kurzbeschreibung (Abstract): | Segmentation of medical images is a prerequisite in clinical practice. Many segmentation algorithms use statistical shape models. Due to the lack of tools providing prior information on the data, standard models are frequently used. However, they do not necessarily describe the data in an optimal way. Model-based segmentation can be supported by Visual Analytics tools, which give the user a deeper insight into the correspondence between data and model result. Combining both approaches, better models for segmentation of organs in medical images are created. In this work, we identify the main tasks and problems in model-based image segmentation. As a proof of concept, we show that already small visual-interactive extensions can be very beneficial. Based on these results, we present research challenges for Visual Analytics in this area. |
| Freie Schlagworte: | Forschungsgruppe Visual Search and Analysis (VISA), Forschungsgruppe Medical Computing (MECO), Business Field: Visual decision support, Research Area: Confluence of graphics and vision, Visual analytics, Medical imaging, Statistical shape models (SSM) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 22 Jul 2021 18:31 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum