Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images

For radiotherapy planning, contouring of target volume and healthy structures at risk in CT volumes is essential. To automate this process, one of the available segmentation techniques can be used for many thoracic organs except the esophagus, which is very hard to segment due to low contrast. In th...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Ročník 2011; s. 3403 - 3406
Hlavní autori: Kurugol, S., Bas, E., Erdogmus, D., Dy, J. G., Sharp, G. C., Brooks, D. H.
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2011
Predmet:
3D Image Segmentation
Algorithms
Atmospheric modeling
Computed tomography
Curve Tracing
Esophagus
Esophagus - diagnostic imaging
Estimation
Humans
Level set
Level Sets
Probability
Radiation Oncology
Shape
Shape Model
Spatial
Three dimensional displays
Tomography, X-Ray Computed - methods
ISBN:9781424441211, 1424441218
ISSN:1094-687X, 2694-0604, 1557-170X, 2694-0604, 1557-170X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:For radiotherapy planning, contouring of target volume and healthy structures at risk in CT volumes is essential. To automate this process, one of the available segmentation techniques can be used for many thoracic organs except the esophagus, which is very hard to segment due to low contrast. In this work we propose to initialize our previously introduced model based 3D level set esophagus segmentation method with a principal curve tracing (PCT) algorithm, which we adapted to solve the esophagus centerline detection problem. To address challenges due to low intensity contrast, we enhanced the PCT algorithm by learning spatial and intensity priors from a small set of annotated CT volumes. To locate the esophageal wall, the model based 3D level set algorithm including a shape model that represents the variance of esophagus wall around the estimated centerline is utilized. Our results show improvement in esophagus segmentation when initialized by PCT compared to our previous work, where an ad hoc centerline initialization was performed. Unlike previous approaches, this work does not need a very large set of annotated training images and has similar performance.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISBN:9781424441211
1424441218
ISSN:1094-687X
2694-0604
1557-170X
2694-0604
1557-170X
DOI:10.1109/IEMBS.2011.6090921