The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access)
A FULLY-AUTOMATED, DEEP LEARNING-BASED FRAMEWORK FOR CT-BASED LOCALIZATION, SEGMENTATION, VERIFICATION AND PLANNING OF METASTATIC VERTEBRAE
Author ORCID Identifier
Date of Graduation
Doctor of Philosophy (PhD)
Palliative radiotherapy is an effective treatment for the palliation of symptoms caused by vertebral metastases. Visible evidence of disease is localized on medical images as part of the treatment planning process. However, complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with wrong level treatments of the spine. In addition, erroneous manual contouring of anatomic structures is a major failure mode in radiotherapy treatment planning.
The purpose of this study is to mitigate the challenges associated with treatment planning of the spine by automating the treatment planning process for three-dimensional conformal radiotherapy. To accomplish this, deep and machine learning models will work in symphony within a multi-stage framework to perform image-based tasks in place of manual tasks. An automated solution that is efficient, effective, and safe would be especially valuable for clinics seeking to expedite their palliative radiotherapy planning services or optimize their use of diagnostic and simulation CT imaging for radiotherapy treatment planning.
The central hypothesis of this work is that that 90% of automated treatment plans for bony metastases of the spine are clinically acceptable and can be generated in less than 10 minutes. Additionally, that potential mistreatment can be flagged with 100% sensitivity and at least 75% specificity.
segmentation, treatment planning, deep learning, palliative radiotherapy, spinal metastases
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