Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition

Yubing Tong, J. K. Udupa, D. Odhner, Sanghun Sin, Raanan Arens

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

5 Citations (Scopus)

Abstract

In studying Obstructive Sleep Apnea Syndrome (OSAS) in obese children, the quantification of obesity through MRI has been shown to be useful. For large-scale studies, interactive or manual segmentation strategies become inadequate. Our goal is to automate this process to facilitate high throughput, precision, and accuracy and to eliminate subjectivity in quantification. In this paper, we demonstrate the adaptation, to this application, of a general body-wide Automatic Anatomy Recognition (AAR) system that is being developed separately. The AAR system has been developed based on existing clinical CT image data of 50-60 year-old male subjects and using fuzzy models of a large number of objects in each body region. The individual objects and their models are arranged in a hierarchy that is specific to each body region. In the application under consideration in this paper, we are primarily interested in only the skin boundary, and subcutaneous and visceral adipose region. Further, the image modality is MRI, and the study subjects are 8-17 year-old females. We demonstrate in this paper that, once such a full AAR system is built, it can be easily adapted to a new application by specifying the objects of interest, their hierarchy, and a few other application-specific parameters. Our tests based on MRI of 14 obese subjects indicate a recognition accuracy of about 2 voxels or better for both types of adipose regions. This seems quite adequate in terms of the initialization of model-based graph-cut (GC) and iterative relative fuzzy connectedness (IRFC) algorithms implemented in our AAR system for subsequent delineation of the objects. Both algorithms achieved low false positive volume fraction (FPVF) and high true positive volume fraction (TPVF), with IRFC performing better than GC.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8672
DOIs
StatePublished - 2013
EventMedical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging - Lake Buena Vista, FL, United States
Duration: Feb 10 2013Feb 13 2013

Other

OtherMedical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityLake Buena Vista, FL
Period2/10/132/13/13

Fingerprint

anatomy
Adiposity
Magnetic resonance imaging
Anatomy
Body Regions
Volume fraction
hierarchies
obesity
Pediatric Obesity
Obstructive Sleep Apnea
sleep
delineation
respiration
Skin
Throughput

Keywords

  • Fuzzy models
  • Object recognition
  • Obstructive sleep apnea
  • Quantification of adiposity
  • Segmentation

ASJC Scopus subject areas

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

Cite this

Tong, Y., Udupa, J. K., Odhner, D., Sin, S., & Arens, R. (2013). Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 8672). [86721R] https://doi.org/10.1117/12.2007938

Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition. / Tong, Yubing; Udupa, J. K.; Odhner, D.; Sin, Sanghun; Arens, Raanan.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8672 2013. 86721R.

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

Tong, Y, Udupa, JK, Odhner, D, Sin, S & Arens, R 2013, Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 8672, 86721R, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, Lake Buena Vista, FL, United States, 2/10/13. https://doi.org/10.1117/12.2007938
Tong Y, Udupa JK, Odhner D, Sin S, Arens R. Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8672. 2013. 86721R https://doi.org/10.1117/12.2007938
Tong, Yubing ; Udupa, J. K. ; Odhner, D. ; Sin, Sanghun ; Arens, Raanan. / Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8672 2013.
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