Autosegmentation for head and neck Radiotherapy

Vanderbilt University, Nashville TN

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Abstract

[unreadable] DESCRIPTION (provided by applicant): 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. [unreadable] [unreadable] [unreadable]