Automated Segmentation of Computed Tomography Images

Justin SenseneyPaul 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– 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• 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

• 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)

• Bone segmentation– Region growing– Bone scattering

• Marrow segmentation

• Fascia, muscle segmentation

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

• 2D options:– Livewire– Level set

• 2D/3D options:– Region growing– B-spline approximations

during slice propagation

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

• Reports– Text– PDF, using iText– Standard output

• MIPAV Statistics generator

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

Customization

• Interface for customized CT projects, options:– New regions of interest– Calculation dependencies– Display options– Calculation options

Start Pane: AbdomenStart Pane: AbdomenStart Voi: AbdomenStart Voi: AbdomenColor: 255,200,0Color: 255,200,0Do_Calc: trueDo_Calc: trueEnd VoiEnd VoiStart Voi: Subcut.Start Voi: Subcut.Color: 255,0,0Color: 255,0,0Do_Calc: trueDo_Calc: trueEnd VoiEnd VoiStart Voi: PhantomStart Voi: PhantomColor: 0,255,0Color: 0,255,0End VoiEnd VoiEnd PaneEnd PaneStart Pane: TissueStart Pane: TissueStart Voi: VisceralStart Voi: VisceralColor: 255,200,0Color: 255,200,0Do_Calc: trueDo_Calc: true

End VoiEnd VoiStart Voi: LiverStart Voi: LiverColor: 255,0,0Color: 255,0,0End VoiEnd VoiStart Voi: Liver cystsStart Voi: Liver cystsNum_Curves: 7Num_Curves: 7Color: 0,255,0Color: 0,255,0Do_Calc: trueDo_Calc: trueDo_Fill: trueDo_Fill: trueEnd VoiEnd VoiStart Voi: Bone sampleStart Voi: Bone sampleColor: 0,255,255Color: 0,255,255End VoiEnd VoiStart Voi: Water sampleStart Voi: Water sampleColor: 255,0,255Color: 255,0,255End VoiEnd VoiEnd PaneEnd Pane

Customization Options

• Orientation invariant• Volume/Area Options• Units Specification

Thigh Results% Difference Standard Deviation

Left Thigh 0.18 0.38

Right Thigh 0.24 0.55

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

• Useful for manually demanding segmentations

Abdomen Results% Difference Standard Deviation

Abdomen 1.12 2.92

Abdomen Fat 2.34 1.92

Subcutaneous Fat 3.06 3.58

Subcutaneous Fat HU 1.57 1.50

• Larger variability

• Needs manual attention

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

• http://mipav.cit.nih.gov– Look in the plugins folder for the

MuscleSegmentation plugin

• SenseneyJ@mail.nih.gov

• Open Source? No….

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?

Watershed?

• Requires pre-processing steps• Limited viability

Future work?

• Algorithms

• Data

• Usability

• Extensibility