Super-mask-based object localization for auto-contouring in head and neck radiation therapy planning

Yubing Tong; Jayaram K. Udupa; Drew A. Torigian

doi:10.1117/12.2511973

8 March 2019 Super-mask-based object localization for auto-contouring in head and neck radiation therapy planning

Yubing Tong, Jayaram K. Udupa, Drew A. Torigian

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 109512L (2019) https://doi.org/10.1117/12.2511973
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

We have presented a variety of methods for object recognition based on the Automatic Anatomy Recognition (AAR) framework at previous SPIE conferences, including AAR recognition via optimal threshold on intensity, AAR recognition via composite information from intensity and texture, and AAR recognition with the optimal hierarchical structure design, and via neural networks to learn object relationships. The purpose of this paper is to introduce new features for the AAR-based recognition procedure and improve the performance of object localization for autocontouring in head and neck (H&N) radiation therapy planning, specifically for some of the most challenging objects. The proposed super-mask technique first registers images used for model building among themselves optimally by using a minimal spanning tree in the complete graph formed with images as nodes to determine the order of registering images. Subsequently, we build a super-mask by combining the similarly registered binary images corresponding to each object by taking (S1) union of all binary images, (S2) intersection among all binary images, or (S3) the votingbased fuzzy mask created by adding the binary images. The super-mask is then used to confine search for optimum localization of the object in the given image. A large-scale H&N computed tomography (CT) data set with 216 subjects and over 2000 3D object samples were utilized in this study. The super-mask-based object localization approach within the AAR framework improved the recognition accuracy by 25-45% compared with the previous AAR strategy, especially for the most challenging H&N objects. On low quality images, the new method achieves recognition accuracy within 2 voxels on 50-60% of the cases.

Citation Download Citation

Yubing Tong, Jayaram K. Udupa, and Drew A. Torigian "Super-mask-based object localization for auto-contouring in head and neck radiation therapy planning", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512L (8 March 2019); https://doi.org/10.1117/12.2511973

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KEYWORDS

Computed tomography

Binary data

Image quality

Neck

Seaborgium

Fuzzy logic

Image segmentation

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