Automated segmentation of computed tomography images
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Automated Segmentation of Computed Tomography Images. Justin Senseney Paul Hemler, PhD Matthew J. McAuliffe, PhD. Introduction. Chronic osteoarthritis risk factors, over time Obesity (NIH: Body Mass Index > 30) BMI discussion Obesity well measured by relative loss of muscle

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Automated Segmentation of Computed Tomography Images

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Automated segmentation of computed tomography images

Automated Segmentation of Computed Tomography Images

Justin Senseney

Paul Hemler, PhD

Matthew J. McAuliffe, PhD


Introduction

Introduction

  • Chronic osteoarthritis risk factors, over time

    • Obesity (NIH: Body Mass Index > 30)

  • BMI discussion

    • Obesity well measured by relative loss of muscle

    • Fat around muscle tissue independent of BMI

  • Need for automated systems to quantitatively measure muscle loss

K. F. Adams, A. Schatzkin, T. B. Harris, V. Kipnis, T. Mouw, R. Ballard-Barbash, A. Hollenbeck, and M. F. Leitzmann. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. New England Journal of Medicine, 355(8):763–778, 2006.


Methods

Methods

  • Automatic segmentation

  • Semiautomatic segmentation

M. McAuliffe, F. Lalonde, D. McGarry, W. Gandler, K. Csaky, and B. Trus. Medical image processing, analysis and visualization in clinical research. In Computer- Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on, pages 381–386, 2001.


Automatic segmentation thigh

Automatic Segmentation – Thigh

  • Thigh segmentation

    • Threshold

  • Connected thigh segmentation

    • Identify separation

S. Ohshima, S. Yamamoto, T. Yamaji, M. Suzuki, M. Mutoh, M. Iwasaki, S. Sasazuki, K. Kotera, S. Tsugane, Y. Muramatsu, and N. Moriyama. Development of an automated 3d segmentation program for volume quantification of body fat distribution using ct. Japanese Journal of Radiological Technology, 64(9):1177–1181, 2008.


Automatic segmentation thigh 2

Automatic Segmentation – Thigh (2)

  • Bone segmentation

    • Region growing

    • Bone scattering

  • Marrow segmentation

  • Fascia, muscle segmentation


Automatic segmentation abdomen

Automatic Segmentation - Abdomen

  • Abdomen segmentation

  • Subcutaneous fat segmentation

    • Method from Zhao, et al.

B. Zhao, J. Colville, J. Kalaigian, S. Curren, J. Li, P. Kijewski, and L. Schwartz. Automated quantification of body fat distribution on volumetric computed tomography. Journal of Computer Assisted Tomography, 30(5), 2006.


Semi automatic segmentation

Semi-Automatic Segmentation

  • 2D options:

    • Livewire

    • Level set

  • 2D/3D options:

    • Region growing

    • B-spline approximations during slice propagation


Tissue classification

Tissue Classification

  • Partial voluming concerns:

    • -190 <= fat pixel <= -30

    • 0 <= muscle pixel <= 100

    • -30 < partial volume pixel < 0

  • Custom look-up table: fat-red, muscle-blue, partial volume-white.

S. Ohshima, S. Yamamoto, T. Yamaji, M. Suzuki, M. Mutoh, M. Iwasaki, S. Sasazuki, K. Kotera, S. Tsugane, Y. Muramatsu, and N. Moriyama. Development of an automated 3d segmentation program for volume quantification of body fat distribution using ct. Japanese Journal of Radiological Technology, 64(9):1177–1181, 2008.


Muscle and fat quantification

Muscle and Fat Quantification

  • Reports

    • Text

    • PDF, using iText

    • Standard output

  • MIPAV Statistics generator

B. Lowagie. iText in Action: Creating and Manipulating PDF. Manning, New York, 2006.


Customization

Customization

  • Interface for customized CT projects, options:

    • New regions of interest

    • Calculation dependencies

    • Display options

    • Calculation options

Start Pane: Abdomen

Start Voi: Abdomen

Color: 255,200,0

Do_Calc: true

End Voi

Start Voi: Subcut.

Color: 255,0,0

Do_Calc: true

End Voi

Start Voi: Phantom

Color: 0,255,0

End Voi

End Pane

Start Pane: Tissue

Start Voi: Visceral

Color: 255,200,0

Do_Calc: true

End Voi

Start Voi: Liver

Color: 255,0,0

End Voi

Start Voi: Liver cysts

Num_Curves: 7

Color: 0,255,0

Do_Calc: true

Do_Fill: true

End Voi

Start Voi: Bone sample

Color: 0,255,255

End Voi

Start Voi: Water sample

Color: 255,0,255

End Voi

End Pane


Customization options

Customization Options

  • Orientation invariant

  • Volume/Area Options

  • Units Specification


Thigh results

Thigh Results

  • Compared to 13 freehand segmented images from the University of California, San Diego (UCSD)

  • Useful for manually demanding segmentations


Abdomen results

Abdomen Results

  • Larger variability

  • Needs manual attention


Conclusion

Conclusion

  • Useful automatic and semi-automatic methods

    • Ability to later refine these

    • Benefits from manual overview

  • Larger analysis set needed

    • Comparison to automatic methods

    • Comparison to other qualified people for manual segmentation


Download

Download

  • http://mipav.cit.nih.gov

    • Look in the plugins folder for the MuscleSegmentation plugin

  • [email protected]

  • Open Source? No….


Acknowledgments

Acknowledgments

This work was supported by the Intramural Research Program of the National Institutes of Health and the Center for Information Technology at the National Institutes of Health.


Questions

Questions?


Watershed

Watershed?

  • Requires pre-processing steps

  • Limited viability


Future work

Future work?

  • Algorithms

  • Data

  • Usability

  • Extensibility


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