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.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Special Emphasis Panel (ZRG1-SBIB-Q (90))
Program Officer
Pai, Vinay Manjunath
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University of Iowa
Engineering (All Types)
Schools of Engineering
Iowa City
United States
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Oguz, Ipek; Sonka, Milan (2014) LOGISMOS-B: layered optimal graph image segmentation of multiple objects and surfaces for the brain. IEEE Trans Med Imaging 33:1220-35
Balocco, Simone; Gatta, Carlo; Ciompi, Francesco et al. (2014) Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 38:70-90
Oguz, Ipek; Sonka, Milan (2014) Robust cortical thickness measurement with LOGISMOS-B. Med Image Comput Comput Assist Interv 17:722-30
Zhang, Li; Sonka, Milan; Folk, James C et al. (2014) Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization. Invest Ophthalmol Vis Sci 55:2329-35
Oguz, Ipek; Zhang, Honghai; Rumple, Ashley et al. (2014) RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI. J Neurosci Methods 221:175-82
Sohn, Elliott H; Chen, John J; Lee, Kyungmoo et al. (2013) Reproducibility of diabetic macular edema estimates from SD-OCT is affected by the choice of image analysis algorithm. Invest Ophthalmol Vis Sci 54:4184-8
Sun, Shanhui; Sonka, Milan; Beichel, Reinhard R (2013) Graph-based IVUS segmentation with efficient computer-aided refinement. IEEE Trans Med Imaging 32:1536-49
Kafieh, Raheleh; Rabbani, Hossein; Abramoff, Michael D et al. (2013) Curvature correction of retinal OCTs using graph-based geometry detection. Phys Med Biol 58:2925-38
Zhang, Honghai; Abiose, Ademola K; Gupta, Dipti et al. (2013) Novel indices for left-ventricular dyssynchrony characterization based on highly automated segmentation from real-time 3-d echocardiography. Ultrasound Med Biol 39:72-88
Sun, Shanhui; Sonka, Milan; Beichel, Reinhard R (2013) Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface. Comput Med Imaging Graph 37:15-27

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