This is a competitive continuation of a project that already yielded the highly flexible, accurate, and broadly applicable LOGISMOS framework for context-aware n-dimensional image segmentation. To substantially improve and extend its capability, we will develop Deep LOGISMOS that combines and reinforces the complementary advantages of LOGISMOS and deep learning (DL). There is growing need for quantitative failure-free 3D and higher-D image analysis for diagnostic and/or planning purposes. Examples of current use exist in radiation oncology, cardiology, ophthalmology and other areas of routine clinical medicine, many of which however still rely on manual slice-by-slice tracing. This manual nature of such analyses hinders their use in precision medicine. Deep LOGISMOS research proposed here will solve this problem and will offer routine efficient analysis of clinical images of analyzable quality. To stimulate a new phase of this research project, we hypothesize that: Advanced graph-based image segmentation algorithms, when combined with deep-learning-derived application/modality specific parameters and allowing highly efficient expert-analyst guidance working in concert with the segmentation algorithms, will significantly increase quantitative analysis performance in routinely acquired, complex, diagnostic-quality medical images across diverse application areas. The proposed research focuses on establishing an image segmentation and analysis framework combining the strengths of LOGISMOS and DL, developing a new way to efficiently generate training data necessary for learning from examples, forming a failure-free strategy for 3D, 4D, and generally n-D quantitative medical image analysis, and discovering ways for automated segmentation quality control. We will fulfill these specific aims: 1. Develop an efficient approach for building large segmentation training datasets in 3D, 4D, n-D using assisted and suggestive annotations. 2. Develop Deep LOGISMOS, combining two well-established algorithmic strategies ? deep learning and LOGISMOS graph search. 3. Develop and validate methods employing deep learning for quality control of Deep LOGISMOS. 4. In healthcare-relevant applications, demonstrate that Deep LOGISMOS improves segmentation performance in comparison with state-of-the-art segmentation techniques. Deep LOGISMOS will bring broadly available routine quantification of clinical images, positively impacting the role of reliable image-based information in tomorrow?s precision medicine.
Previous phases of this successful research project were devoted to the development of new graph-based methods for multi-surface and/or multi-object multi-dimensional biomedical image segmentation. Deep learning is emerging as an important new way to learn from large sets of examples. This proposal will combine deep learning and graph-based image analysis to maximize their combined strengths and overcome their individual weaknesses with the overarching goal to facilitate routine use of quantitative medical image analysis techniques in personalized medical care.
|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|
|Beichel, Reinhard R; Van Tol, Markus; Ulrich, Ethan J et al. (2016) Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Med Phys 43:2948-2964|
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