Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

Fengxia Yan; Jayaram K. Udupa; Yubing Tong; Guoping Xu; Dewey Odhner; Drew A. Torigian

doi:10.1117/12.2293700

2 March 2018 Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

Fengxia Yan, Jayaram K. Udupa, Yubing Tong, Guoping Xu, Dewey Odhner, Drew A. Torigian

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105742O (2018) https://doi.org/10.1117/12.2293700
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition–delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

Citation Download Citation

Fengxia Yan, Jayaram K. Udupa, Yubing Tong, Guoping Xu, Dewey Odhner, and Drew A. Torigian "Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742O (2 March 2018); https://doi.org/10.1117/12.2293700

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KEYWORDS

Neural networks

Antimony

Seaborgium

Computed tomography

Medical imaging

Neck

Neurons

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