Automatic anatomy recognition of sparse objects

Liming Zhao; Jayaram K. Udupa; Dewey Odhner; Huiqian Wang; Yubing Tong; Drew A. Torigian

doi:10.1117/12.2082567

20 March 2015 Automatic anatomy recognition of sparse objects

Liming Zhao, Jayaram K. Udupa, Dewey Odhner, Huiqian Wang, Yubing Tong, Drew A. Torigian

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94133N (2015) https://doi.org/10.1117/12.2082567
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

A general body-wide automatic anatomy recognition (AAR) methodology was proposed in our previous work based on hierarchical fuzzy models of multitudes of objects which was not tied to any specific organ system, body region, or image modality. That work revealed the challenges encountered in modeling, recognizing, and delineating sparse objects throughout the body (compared to their non-sparse counterparts) if the models are based on the object’s exact geometric representations. The challenges stem mainly from the variation in sparse objects in their shape, topology, geographic layout, and relationship to other objects. That led to the idea of modeling sparse objects not from the precise geometric representations of their samples but by using a properly designed optimal super form. This paper presents the underlying improved methodology which includes 5 steps: (a) Collecting image data from a specific population group G and body region Β and delineating in these images the objects in Β to be modeled; (b) Building a super form, S-form, for each object O in Β; (c) Refining the S-form of O to construct an optimal (minimal) super form, S*-form, which constitutes the (fuzzy) model of O; (d) Recognizing objects in Β using the S*-form; (e) Defining confounding and background objects in each S*-form for each object and performing optimal delineation. Our evaluations based on 50 3D computed tomography (CT) image sets in the thorax on four sparse objects indicate that substantially improved performance (FPVF~2%, FNVF~10%, and success where the previous approach failed) can be achieved using the new approach.

Citation Download Citation

Liming Zhao, Jayaram K. Udupa, Dewey Odhner, Huiqian Wang, Yubing Tong, and Drew A. Torigian "Automatic anatomy recognition of sparse objects", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133N (20 March 2015); https://doi.org/10.1117/12.2082567

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