Auto-contouring via automatic anatomy recognition of organs at risk in head and neck cancer on CT images

Xingyu Wu; Jayaram K. Udupa; Yubing Tong; Dewey Odhner; Gargi V. Pednekar; Charles B. Simone; David McLaughlin; Chavanon Apinorasethkul; John Lukens; Dimitris Mihailidis; Geraldine Shammo; Paul James; Joseph Camaratta; Drew A. Torigian

doi:10.1117/12.2293946

13 March 2018 Auto-contouring via automatic anatomy recognition of organs at risk in head and neck cancer on CT images

Xingyu Wu, Jayaram K. Udupa, Yubing Tong, Dewey Odhner, Gargi V. Pednekar, Charles B. Simone, David McLaughlin, Chavanon Apinorasethkul, John Lukens, Dimitris Mihailidis, Geraldine Shammo, Paul James, Joseph Camaratta, Drew A. Torigian

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Proceedings Volume 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling; 1057617 (2018) https://doi.org/10.1117/12.2293946
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H and N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images. Our method has three major steps: model building, object recognition, and object delineation. First, the better-quality images from our cohort of H and N CT studies are used to build fuzzy models and find the optimal hierarchy for arranging objects based on the relationship between objects. Then, the object recognition step exploits the rich prior anatomic information encoded in the hierarchy to derive the location and pose for each object, which leads to generalizable and robust methods and mitigation of object localization challenges. Finally, the delineation algorithms employ local features to contour the boundary based on object recognition results. We make several improvements within the AAR framework, including finding recognition-error-driven optimal hierarchy, modeling boundary relationships, combining texture and intensity, and evaluating object quality. Experiments were conducted on the largest ensemble of clinical data sets reported to date, including 216 planning CT studies and over 2,600 object samples. The preliminary results show that on data sets with minimal (<4 slices) streak artifacts and other deviations, overall recognition accuracy reaches 2 voxels, with overall delineation Dice coefficient close to 0.8 and Hausdorff Distance within 1 voxel.

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Xingyu Wu, Jayaram K. Udupa, Yubing Tong, Dewey Odhner, Gargi V. Pednekar, Charles B. Simone, David McLaughlin, Chavanon Apinorasethkul, John Lukens, Dimitris Mihailidis, Geraldine Shammo, Paul James, Joseph Camaratta, and Drew A. Torigian "Auto-contouring via automatic anatomy recognition of organs at risk in head and neck cancer on CT images", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057617 (13 March 2018); https://doi.org/10.1117/12.2293946

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