Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations

Ayushi Sinha, Simon Leonard, Austin Reiter, Masaru Ishii, Russell H Taylor, Gregory Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an automatic segmentation and statistical shape modeling system for the paranasal sinuses which allows us to locate structures in and around the sinuses, as well as to observe the variability in these structures. This system involves deformably registering a given patient image to a manually segmented template image, and using the resulting deformation field to transfer labels from the template to the patient image. We use 3D snake splines to correct errors in this initial segmentation. Once we have several accurately segmented images, we build statistical shape models to observe the population mean and variance for each structure. These shape models are useful to us in several ways. Regular registration methods are insufficient to accurately register pre-operative computed tomography (CT) images with intra-operative endoscopy video of the sinuses. This is because of deformations that occur in structures containing erectile tissue. Our aim is to estimate these deformations using our shape models in order to improve video-CT registration, as well as to distinguish normal variations in anatomy from abnormal variations, and automatically detect and stage pathology. We can also compare the mean shapes and variances in different populations, such as different genders or ethnicities, in order to observe differences and similarities, as well as in different age groups in order to observe the developmental changes that occur in the sinuses.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016: Image Processing
PublisherSPIE
Volume9784
ISBN (Electronic)9781510600195
DOIs
StatePublished - 2016
EventMedical Imaging 2016: Image Processing - San Diego, United States
Duration: Mar 1 2016Mar 3 2016

Other

OtherMedical Imaging 2016: Image Processing
CountryUnited States
CitySan Diego
Period3/1/163/3/16

Fingerprint

paranasal sinuses
Paranasal Sinuses
sinuses
Tomography
Snakes
Statistical Models
estimates
Population
Endoscopy
Anatomy
Age Groups
Pathology
templates
Splines
tomography
Labels
snakes
Tissue
anatomy
registers

Keywords

  • Erectile tissue
  • Natural variation
  • Paranasal sinuses
  • Registration
  • Segmentation
  • Statistical shape model
  • Turbinates
  • Variance

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations. / Sinha, Ayushi; Leonard, Simon; Reiter, Austin; Ishii, Masaru; Taylor, Russell H; Hager, Gregory.

Medical Imaging 2016: Image Processing. Vol. 9784 SPIE, 2016. 97840D.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sinha, A, Leonard, S, Reiter, A, Ishii, M, Taylor, RH & Hager, G 2016, Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations. in Medical Imaging 2016: Image Processing. vol. 9784, 97840D, SPIE, Medical Imaging 2016: Image Processing, San Diego, United States, 3/1/16. https://doi.org/10.1117/12.2217337
Sinha, Ayushi ; Leonard, Simon ; Reiter, Austin ; Ishii, Masaru ; Taylor, Russell H ; Hager, Gregory. / Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations. Medical Imaging 2016: Image Processing. Vol. 9784 SPIE, 2016.
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