Abstract
This paper presents a cranial nerve segmentation technique that combines a 3D deformable contour and a 3D contour Statistical Shape Model (SSM). A set of training data for the construction of the 3D contour shape model is produced using a 1-simplex based discrete deformable contour model where the centerline identification proceeds by optimizing internal and external forces. Point-correspondence for the training dataset is performed using an entropybased energy minimization of particles on the centerline curve. The resulting average shape is used as a prior knowledge, which is incorporated into the 1-simplex as a reference shape model, making the approach stable against low resolution and image artifacts during segmentation using MRI data. Shape variability is shown using the first 3 modes of variation. The segmentation result is validated quantitatively, with ground truth provided by an expert.
| Original language | English (US) |
|---|---|
| Title of host publication | Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings |
| Publisher | Springer Verlag |
| Pages | 68-76 |
| Number of pages | 9 |
| Volume | 9958 LNCS |
| ISBN (Print) | 9783319464718 |
| DOIs | |
| State | Published - 2016 |
| Event | 5th International Workshop on Clinical Image-Based Procedures, CLIP 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: Oct 17 2016 → Oct 17 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9958 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 5th International Workshop on Clinical Image-Based Procedures, CLIP 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 10/17/16 → 10/17/16 |
Fingerprint
Keywords
- Centerline
- Contour model
- Cranial nerves
- Patient-specific segmentation
- Statistical Shape Model
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Towards a statistical shape-aware deformable contour model for cranial nerve identification. / Sultana, Sharmin; Agrawal, Praful; Elhabian, Shireen Y.; Whitaker, Ross T.; Rashid, Tanweer; Blatt, Jason E.; Cetas, Justin; Audette, Michel A.
Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9958 LNCS Springer Verlag, 2016. p. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9958 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Towards a statistical shape-aware deformable contour model for cranial nerve identification
AU - Sultana, Sharmin
AU - Agrawal, Praful
AU - Elhabian, Shireen Y.
AU - Whitaker, Ross T.
AU - Rashid, Tanweer
AU - Blatt, Jason E.
AU - Cetas, Justin
AU - Audette, Michel A.
PY - 2016
Y1 - 2016
N2 - This paper presents a cranial nerve segmentation technique that combines a 3D deformable contour and a 3D contour Statistical Shape Model (SSM). A set of training data for the construction of the 3D contour shape model is produced using a 1-simplex based discrete deformable contour model where the centerline identification proceeds by optimizing internal and external forces. Point-correspondence for the training dataset is performed using an entropybased energy minimization of particles on the centerline curve. The resulting average shape is used as a prior knowledge, which is incorporated into the 1-simplex as a reference shape model, making the approach stable against low resolution and image artifacts during segmentation using MRI data. Shape variability is shown using the first 3 modes of variation. The segmentation result is validated quantitatively, with ground truth provided by an expert.
AB - This paper presents a cranial nerve segmentation technique that combines a 3D deformable contour and a 3D contour Statistical Shape Model (SSM). A set of training data for the construction of the 3D contour shape model is produced using a 1-simplex based discrete deformable contour model where the centerline identification proceeds by optimizing internal and external forces. Point-correspondence for the training dataset is performed using an entropybased energy minimization of particles on the centerline curve. The resulting average shape is used as a prior knowledge, which is incorporated into the 1-simplex as a reference shape model, making the approach stable against low resolution and image artifacts during segmentation using MRI data. Shape variability is shown using the first 3 modes of variation. The segmentation result is validated quantitatively, with ground truth provided by an expert.
KW - Centerline
KW - Contour model
KW - Cranial nerves
KW - Patient-specific segmentation
KW - Statistical Shape Model
UR - http://www.scopus.com/inward/record.url?scp=84994096887&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994096887&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46472-5_9
DO - 10.1007/978-3-319-46472-5_9
M3 - Conference contribution
SN - 9783319464718
VL - 9958 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 68
EP - 76
BT - Clinical Image-Based Procedures: Translational Research in Medical Imaging - 5th International Workshop, CLIP 2016 held in conjunction with MICCAI 2016, Proceedings
PB - Springer Verlag
ER -