This is a competitive continuation of our Phase-II project. After successfully fulfilling all of its aims, our framework for optimal multi-surface andor multi-object n-D biomedical image segmentation was further extended, validated, and its practical utility demonstrated in clinical and translational image analysis tasks. This Phase-III proposal will develop several important extensions addressing identified limitations of the current framework and specifically focusing on applicability of the methodology to translational and routine healthcare tasks. Novel methods will be developed for simultaneous segmentation of mutually interacting regions and surfaces, automated design of cost functions from segmentation examples, and overcoming failures of automated techniques in routine diagnostic quality images by allowing limited and highly efficient expert input to guide the image segmentation processes. We hypothesize that advanced graph-based image segmentation algorithms merging machine- learning-derived segmentation parameters and image-specific expert guidance will significantly increase quantitative analysis performance in routinely acquired complex diagnostic-quality medical images across diverse application areas. We propose to: 1) Develop 3D, 4D, and generally n-D approaches for simultaneous segmentation of mutually interacting regions (objects) and surfaces. 2) Develop methods for data-driven automated design of cost functions used for surface-based, region-based, and surface-and-region-based graph search image segmentation. 3) Develop "Just-Enough-Interaction" (JEI) approaches for efficient "real-time" medical image segmentation, thus achieving robust clinical applicability of quantitative medical image analysis. 4) Assess performance of all developed methods in translational research settings;determine performance in quantitative medical image analysis and radiation oncology treatment planning workflow. As a result, our project will enable routine quantification and therefore personalized care.
Phases I and II of this research project multi-surface and/or multi-object n-D biomedical image segmentation and demonstrated its practical utility. This Phase-III proposal will develop important extensions leading to higher flexibility and healthcare utility of the developed methods, facilitating routine use of quantitative medical image analysis i personalized medical care.
|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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