We propose to develop, implement, and test the methodology required to automate the segmentation of structures in the treatment planning images of patients with intracranial and head- and-neck cancers. Delineating critical structures for radiotherapy of the brain is required for advanced radiotherapy technologies to determine if the dose from the proposed treatment will impair the functionality of the structures. Employing an automatic segmentation computer module in the radiation oncology treatment planning process has the potential to significantly increase the efficiency, cost- effectiveness, and, ultimately, clinical outcome of patients undergoing radiation therapy. Such a system would address the formidable labor- and time-intensive challenges associated with the current practice of manually delineating normal anatomical structures on the serial slices of treatment planning images. Specifically, we propose to (1) further improve, implement, and test the semi-automatic atlas-based segmentation algorithms we have developed at our institution for the contouring of intracranial structures and substructures for the treatment of patients with small to moderate size intracranial tumors, (2) to develop, implement, and test atlas-based segmentation algorithms for the contouring of intracranial structures and substructures for the treatment of patients with large space-occupying intracranial tumors, and (3) to quantify the reduction in user-interaction time afforded by these methods in the clinical setting.
| Hu, Peijun; Huo, Yuankai; Kong, Dexing et al. (2018) Automated Characterization of Body Composition and Frailty with Clinically Acquired CT. Comput Methods Clin Appl Musculoskelet Imaging (2017) 10734:25-35 |
| Huo, Yuankai; Asman, Andrew J; Plassard, Andrew J et al. (2017) Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp 38:599-616 |
| Huo, Yuankai; Plassard, Andrew J; Carass, Aaron et al. (2016) Consistent cortical reconstruction and multi-atlas brain segmentation. Neuroimage 138:197-210 |
| Xu, Zhoubing; Baucom, Rebeccah B; Abramson, Richard G et al. (2016) Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set. Proc SPIE Int Soc Opt Eng 9784: |
| Xu, Zhoubing; Gertz, Adam L; Burke, Ryan P et al. (2016) Improving Spleen Volume Estimation Via Computer-assisted Segmentation on Clinically Acquired CT Scans. Acad Radiol 23:1214-20 |
| Huo, Yuankai; Carass, Aaron; Resnick, Susan M et al. (2016) Combining Multi-atlas Segmentation with Brain Surface Estimation. Proc SPIE Int Soc Opt Eng 9784: |
| Xu, Zhoubing; Panjwani, Sahil A; Lee, Christopher P et al. (2016) Evaluation of Body-Wise and Organ-Wise Registrations For Abdominal Organs. Proc SPIE Int Soc Opt Eng 9784: |
| Xu, Zhoubing; Lee, Christopher P; Heinrich, Mattias P et al. (2016) Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT. IEEE Trans Biomed Eng 63:1563-72 |
| Xu, Zhoubing; Conrad, Benjamin N; Baucom, Rebeccah B et al. (2016) Abdomen and spinal cord segmentation with augmented active shape models. J Med Imaging (Bellingham) 3:036002 |
| Xu, Zhoubing; Burke, Ryan P; Lee, Christopher P et al. (2015) Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning. Med Image Anal 24:18-27 |
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