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3D Modeling, Simulation and Segmentation in Medical
Images
Volume modeling, simulation and segmentation are important parts
of computer based medical applications for diagonsis and analysis
of anatomical data. With rapid advances in medical imaging modalities
and volume visualization techniques, computer based diagnosis is becoming
a reality. These computer based tools allow scientists and physicians
to understand and diagnose anatomical structures by virtually interacting
with them. 3D modeling, simulation and segmentaition play a critical
role by facilitating automatic extraction and visualization of the
anatomical organ or interested objects. We developed a 3D deformal
model, Metamorphs, which integrates region texture constraints so
as to achieve more robust segmentation. Compared with traditional
shape-based models, Metamorphs segmentation result is less dependent
on model initialization and not sensitive to noise and spurious edges
inside the object of interest. Then, we further extend it Active Volume
Model (AVM), a similar and improved approach for 3D segmentation.
The shape of this 3D model is considered as an elastic solid, with
a simplex-mesh surface made of thousands of vertices. Efficient optimization
and fast convergence of the model are achieved using the Finite Element
Method (FEM). To further improve segmentation performance, a multiple-surface
constraint is also employed to incorporate spatial constraints among
multiple objects. Several applications are shown to demonstrate the
benefits of these segmentation algorithms based on deformable models
that integrate multiple sources of constraints.
3D Modeling
and Segmentation
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Figure.
3D modeling for Heart (Left), Lung (Middle) and multiple organs
in whole body (Right). |
3D
Heart
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Figure.
3D heart surface segmentation; the distance range
is 2–25 voxels, (1) (a)–(e) deformation
progress of inner surfaces, (2) (a)–(e) outer
surface; (a) Initial model after (b) 3, (c) 9, (d)
21, (e) 27 (converged result) iterations; (1)(f) initial
model in a 2D slice, (2)(f ) converged result in a
2D slice. |
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Figure.
3D lung surface segmentation; the distance range is
3–45 voxels, (1) (a)–(e) deformation progress
of inner surfaces, (2) (a)–(e) outer surface;
(a) Initial model after (b) 3, (c) 9, (d) 21, (e)
26 (converged result) iterations; (1)(f) initial model
in a 2D slice, (2)(f ) converged result in a 2D slice. |
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Figure.
GM and WM segmentation using AVM. The GM and
WM model surfaces each has 131,074 control vertices.
(a)(1) Initial model of GM, (2) after three
iterations, (3) after 12 iterations, (4) after
24 iterations, (5), (6) final converged result
after 36 iterations. (b)(1) Initial model of
WM, (2) after three iterations, (3) after 12
iterations, (4) after 24 iterations, (5), (6)
final converged result after 39 iterations |
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Figure.
Comparing AVM with geodesic active contours (GAC)
and level set evolution without reinitialization (LSEWR).
(a) AVM, (b) GAC, (c) LSEWR. (1) heart LV segmentation,
(2) lung segmentation, (3) brain GM segmentation,
(4) brain WM segmentation. |
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Figure.
Segmentation Demos: (left) 3D liver segmentation
in low-dose CT; (right) 3D Rodent Rain Segmentation
in MRM. |
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Lin Zhong, Shaoting Zhang, Mingchen Gao,
Junzhou Huang, Zhen Qian, Dimitris Metaxas
and Leon Axel, " Papillary
Muscles Analysis from High Resolution CT using Spatial-Temporal
Skeleton Extraction", In Proc. of the IEEE International
Symposium on Biomedical Imaging, ISBI'13, San Francisco, CA,
USA, April 2013.
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Shaoting Zhang, Yiqiang Zhan, Maneesh Dewan,
Junzhou Huang, Dimitris Metaxas and Xiang
Zhou, " Toward
Robust and Effective Shape Modeling: Sparse Shape Composition",
Medical Image Analysis, Volume 16, Issue 1, pp. 265-277, January
2012.
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Xinyi Cui, Shaoting Zhang, Junzhou
Huang, Xiaolei Huang and Dimitris Metaxas, Leon Axel,
" Left Endocardium Segmentation
using Spatio-temporal Metamorphs", In Proc. of the
IEEE International Symposium on Biomedical Imaging, ISBI'12,
Barcelona, Spain, May 2012.
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Tian Shen, Xiaoleo Huang,
Hongsheng Li, Edward Kim, Shaoting Zhang
and Junzhou Huang, " A
3D Laplacian-Driven Parametric Deformable
Model", In Proc. of the 13th International
Conference on Computer Vision, ICCV'11,
Barcelona, Spain, November 2011.
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Shaoting Zhang, Junzhou
Huang, Mustafa Uzunbas, Tian Shen,
Foteini Delis, Xiaolei Huang, Nora Volkow, Panayotis
Thanos and Dimitris N. Metaxas, " 3D
Segmentation of Rodent Brain Structures Using
Hierarchical Shape Priors and Deformable Models",
In Proc. of the 14th Annual International Conference
on Medical Image Computing and Computer Assisted
Intervention, MICCAI'11, Toronto, Canada, September
2011.
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Shaoting Zhang, Yiqiang Zhan,
Maneesh Dewan, Junzhou Huang,
Dimitris Metaxas and Xiang Zhou, " Deformable
Segmentation via Sparse Shape Composition: Towards
the Robustness to Weak Appearance Cues",
In Proc. of the 14th Annual International Conference
on Medical Image Computing and Computer Assisted
Intervention, MICCAI'11, Toronto, Canada, September
2011. ( MICCAI
Young Scientist Award Finalist)
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Shaoting Zhang, Yiqiang Zhan,
Maneesh Dewan, Junzhou Huang,
Dimitris Metaxas and Xiang Zhou, " Sparse
Shape Composition: A New Framework for Shape
Prior Modeling", In Proc. of the IEEE
Computer Society Conference on Computer Vision
and Pattern Recognition, CVPR'11, Colorado Springs,
Colorado, USA, June 2011.
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Mingchen Gao, Junzhou
Huang, Shaoting Zhang, Zhen Qian, Szilard
Voros, Dimitri Metaxas, Leon Axel, " 4D
Cardiac Reconstruction Using High Resolution
CT Images", In Proc. of the Sixth International
Conference on Functional Imaging and Modeling
of the Heart, FIMH'11, New York, USA, May 2011.
( Best Paper Award)
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Shaoting Zhang, Mustafa Uzunbas,
Zhennan Yan, Mingchen Gao, Junzhou Huang,
Dimitri Metaxas, Leon Axel, " Construction
of Left Ventricle 3D Shape Atlas from Cardiac
MRI", In Proc. of the Sixth International
Conference on Functional Imaging and Modeling
of the Heart, FIMH'11, New York, USA, May 2011.
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Shaoting Zhang, Junzhou
Huang, Mustafa Uzunbas, Tian Shen,
Foteini Delis, Xiaolei Huang, Nora Volkow, Panayotis
Thanos, Dimitris Metaxas, " 3D
Segmentation of Rodent Brain Structures Using
Active Volume Model With Shape Priors",
In Proc. of IEEE International Symposium on
Biomedical Imaging: From Nano to Macro, ISBI'11,
Chicago, Illinois, USA, March 2011.
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Tian Shen, Yaoyao Zhu, Xiaolei
Huang, Junzhou Huang, Dimitris
Metaxas, Leon Axel, " Active
Volume Models with Probabilistic Object Boundary
Prediction Module", In Proc. of the
11th Annual International Conf. on Medical Image
Computing and Computer Assisted Intervention,
MICCAI’08, LNCS-5241, pp. 331-341, 2008.
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