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 multiplesurface 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

Project Narrative 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)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (90))
Program Officer
Zhang, Yantian
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Iowa
Engineering (All Types)
Schools of Engineering
Iowa City
United States
Zip Code
Zhang, Ling; Wahle, Andreas; Chen, Zhi et al. (2018) Predicting Locations of High-Risk Plaques in Coronary Arteries in Patients Receiving Statin Therapy. IEEE Trans Med Imaging 37:151-161
Kashyap, Satyananda; Zhang, Honghai; Rao, Karan et al. (2018) Learning-Based Cost Functions for 3-D and 4-D Multi-Surface Multi-Object Segmentation of Knee MRI: Data From the Osteoarthritis Initiative. IEEE Trans Med Imaging 37:1103-1113
Chen, Zhi; Pazdernik, Michal; Zhang, Honghai et al. (2018) Quantitative 3D Analysis of Coronary Wall Morphology in Heart Transplant Patients: OCT-Assessed Cardiac Allograft Vasculopathy Progression. Med Image Anal 50:95-105
Zhang, Ling; Kong, Hui; Liu, Shaoxiong et al. (2017) Graph-based segmentation of abnormal nuclei in cervical cytology. Comput Med Imaging Graph 56:38-48
Kovarnik, Tomas; Chen, Zhi; Mintz, Gary S et al. (2017) Plaque volume and plaque risk profile in diabetic vs. non-diabetic patients undergoing lipid-lowering therapy: a study based on 3D intravascular ultrasound and virtual histology. Cardiovasc Diabetol 16:156
Guo, Zhihui; Kashyap, Satyananda; Sonka, Milan et al. (2017) Machine learning in a graph framework for subcortical segmentation. Proc SPIE Int Soc Opt Eng 10133:
Klein, Barbara E K; Johnson, Chris A; Meuer, Stacy M et al. (2017) Nerve Fiber Layer Thickness and Characteristics Associated with Glaucoma in Community Living Older Adults: Prelude to a Screening Trial? Ophthalmic Epidemiol 24:104-110
Guo, Zhihui; Kwon, Young H; Lee, Kyungmoo et al. (2017) Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma. Invest Ophthalmol Vis Sci 58:3975-3985
Miri, Mohammad Saleh; Robles, Victor A; Abràmoff, Michael D et al. (2017) Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes. Comput Med Imaging Graph 55:87-94
Philip, Ana-Maria; Gerendas, Bianca S; Zhang, Li et al. (2016) Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation. Br J Ophthalmol 100:1372-6

Showing the most recent 10 out of 99 publications