Automatic anatomy recognition via fuzzy object models

Jayaram K. Udupa; Dewey Odhner; Alexandre X. Falcão; Krzysztof Chris Ciesielski; Paulo A. V. Miranda; Monica Matsumoto; George J. Grevera; Babak Saboury; Drew A. Torigian

doi:10.1117/12.911580

16 February 2012 Automatic anatomy recognition via fuzzy object models

Jayaram K. Udupa, Dewey Odhner, Alexandre X. Falcão, Krzysztof Chris Ciesielski, Paulo A. V. Miranda, Monica Matsumoto, George J. Grevera, Babak Saboury, Drew A. Torigian

Author Affiliations +

Proceedings Volume 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling; 831605 (2012) https://doi.org/10.1117/12.911580
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) during radiological image reading becomes essential. As part of this larger goal, last year at this conference we presented a fuzzy strategy for building body-wide group-wise anatomic models. In the present paper, we describe the further advances made in fuzzy modeling and the algorithms and results achieved for AAR by using the fuzzy models. The proposed AAR approach consists of three distinct steps: (a) Building fuzzy object models (FOMs) for each population group G. (b) By using the FOMs to recognize the individual objects in any given patient image I under group G. (c) To delineate the recognized objects in I. This paper will focus mostly on (b). FOMs are built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. The hierarchical pose relationships from the parent to offspring are codified in the FOMs. Several approaches are being explored currently, grouped under two strategies, both being hierarchical: (ra1) those using search strategies; (ra2) those strategizing a one-shot approach by which the model pose is directly estimated without searching. Based on 32 patient CT data sets each from the thorax and abdomen and 25 objects modeled, our analysis indicates that objects do not all scale uniformly with patient size. Even the simplest among the (ra2) strategies of recognizing the root object and then placing all other descendants as per the learned parent-to-offspring pose relationship bring the models on an average within about 18 mm of the true locations.

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Jayaram K. Udupa, Dewey Odhner, Alexandre X. Falcão, Krzysztof Chris Ciesielski, Paulo A. V. Miranda, Monica Matsumoto, George J. Grevera, Babak Saboury, and Drew A. Torigian "Automatic anatomy recognition via fuzzy object models", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 831605 (16 February 2012); https://doi.org/10.1117/12.911580

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