Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 |
| Untertitel | 11th International Conference, Proceedings |
| Seiten | 227-234 |
| Seitenumfang | 8 |
| Auflage | PART 1 |
| ISBN (elektronisch) | 978-3-540-85988-8 |
| Publikationsstatus | Veröffentlicht - 2008 |
| Veranstaltung | 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, USA / Vereinigte Staaten Dauer: 6 Sept. 2008 → 10 Sept. 2008 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Nummer | PART 1 |
| Band | 5241 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Including prior shape in the form of anatomical models is a well-known approach for improving segmentation results in medical images. Currently, most approaches are focused on the modeling and segmentation of individual objects. In case of object constellations, a simultaneous segmentation of the ensemble that uses not only prior knowledge of individual shapes but also additional information about spatial relations between the objects is often beneficial. In this paper, we present a two-scale framework for the modeling and segmentation of the spine as an example for object constellations. The global spine shape is expressed as a consecution of local vertebra coordinate systems while individual vertebrae are modeled as triangulated surface meshes. Adaptation is performed by attracting the model to image features but restricting the attraction to a former learned shape. With the developed approach, we obtained a segmentation accuracy of 1.0 mm in average for ten thoracic CT images improving former results.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 : 11th International Conference, Proceedings. PART 1. Aufl. 2008. S. 227-234 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 5241 LNCS, Nr. PART 1).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Spine Segmentation Using Articulated Shape Models.
AU - Klinder, Tobias
AU - Wolz, Robin
AU - Lorenz, Cristian
AU - Franz, Astrid
AU - Ostermann, Jörn
PY - 2008
Y1 - 2008
N2 - Including prior shape in the form of anatomical models is a well-known approach for improving segmentation results in medical images. Currently, most approaches are focused on the modeling and segmentation of individual objects. In case of object constellations, a simultaneous segmentation of the ensemble that uses not only prior knowledge of individual shapes but also additional information about spatial relations between the objects is often beneficial. In this paper, we present a two-scale framework for the modeling and segmentation of the spine as an example for object constellations. The global spine shape is expressed as a consecution of local vertebra coordinate systems while individual vertebrae are modeled as triangulated surface meshes. Adaptation is performed by attracting the model to image features but restricting the attraction to a former learned shape. With the developed approach, we obtained a segmentation accuracy of 1.0 mm in average for ten thoracic CT images improving former results.
AB - Including prior shape in the form of anatomical models is a well-known approach for improving segmentation results in medical images. Currently, most approaches are focused on the modeling and segmentation of individual objects. In case of object constellations, a simultaneous segmentation of the ensemble that uses not only prior knowledge of individual shapes but also additional information about spatial relations between the objects is often beneficial. In this paper, we present a two-scale framework for the modeling and segmentation of the spine as an example for object constellations. The global spine shape is expressed as a consecution of local vertebra coordinate systems while individual vertebrae are modeled as triangulated surface meshes. Adaptation is performed by attracting the model to image features but restricting the attraction to a former learned shape. With the developed approach, we obtained a segmentation accuracy of 1.0 mm in average for ten thoracic CT images improving former results.
UR - http://www.scopus.com/inward/record.url?scp=58849086415&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85988-8_28
DO - 10.1007/978-3-540-85988-8_28
M3 - Conference contribution
C2 - 18979752
AN - SCOPUS:58849086415
SN - 354085987X
SN - 9783540859871
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 227
EP - 234
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008
T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Y2 - 6 September 2008 through 10 September 2008
ER -