This is a competitive continuation of our Phase-I project. After successfully fulfilling all of its aims, a novel framework for optimal multi-surface and/or multi-object n-D biomedical image segmentation was developed, validated, and its practical utility demonstrated in clinical and translational image analysis tasks. This Phase-II proposal will develop several important extensions addressing identified limitations of the original framework while maintaining the ability of detecting optimal single and multiple interacting surfaces in n-D, including cylindrical shapes, closed-surface shapes, and shapes of complex topology. Novel methods will be developed for incorporation of shape-based a priori knowledge;substantial improvement of processing speed;and for interactive operator-guided segmentation. We hypothesize that by representing the segmentation problem in an arc-weighted graph (instead of the so-far utilized node-weighted graph), the 3-D and 4-D multi-surface multi-object optimal graph searching will offer significantly increased segmentation accuracy and robustness in volumetric image data from a variety of medical imaging sources, offering flexibility and higher processing speed, leading to real-time interactivity and practical applicability. We propose to: 1) Develop and validate a single- and multiple-surface n-D graph-based optimal segmentation method that uses arc-based graph representation, incorporates a priori shape knowledge using hard and soft constraints, and provides shape guidance while utilizing weighted combinations of edge-, region-, and shape-based costs. 2) Develop an approach for parallel (multi-core, multi-threaded) optimal graph search to significantly increase the processing speed and thus improving the method's applicability to higher-dimensional, multiply interacting, and overall larger problems. 3) Develop and evaluate an efficient real-time approach for interactive use of single- and multiple surface segmentations incorporating expert-user guidance while maintaining highly automated character of 3-D or 4-D segmentation. The developed methods will be evaluated against the Phase-I methods to demonstrate statistically significant performance improvements in a variety of tasks with data samples of sufficient sizes.

Public Health Relevance

Three- and four-dimensional (3D + time) analysis of medical image data from MR, CT, ultrasound, or OCT scanners is still performed visually and frequently either non- quantitatively, or only in 2-D slices. Clearly, the 3-D character of the image data provides additional information that may be overlooked by current approaches. The proposed research work is for development of globally optimal image segmentation methods that are practical in 3-D, 4-D and generally n-D medical image data. As such, the study has a promise for facilitating routine clinical analyses of volumetric data from medical image scanners.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB004640-07
Application #
8309340
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (90))
Program Officer
Pai, Vinay Manjunath
Project Start
2004-12-01
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2014-07-31
Support Year
7
Fiscal Year
2012
Total Cost
$370,415
Indirect Cost
$104,476
Name
University of Iowa
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52242
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