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Abstract #2737

Skull Segmentation for MR-Only Radiotherapy Simulation using An Unsupervised-Learning Multi-Sequence Analysis Framework

Max W.K. Law1, Jing Yuan1, Oilei O.L. Wong1, and Ben S.K. Yu1

1Medical Physics & Research Department, Hong Kong Sanatorium & Hospital, Hong Kong Island, Hong Kong

MR-only simulation is increasingly more popular because of superior soft-tissue contrast and radiation dose-free for conventional and adaptive radiotherapy, as compared to CT simulation. Identifying bones is crucial towards successful MR-only simulation, particularly in cranial and head-and-neck regions where radio-sensitive soft-tissues densely present. This abstract proposed a framework exhibiting self-learning compatibility to capture case-specific information to perform skull segmentation. Without manual input and training information, the proposed framework utilized a clustering technique to collectively analyze images from multiple MR sequences. Evaluated in eight volunteer cases, it was shown that the proposed unsupervised-learning framework well-suited MR-based skull segmentation.

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