Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

Yu Liu; Jayaram K. Udupa; Dewey Odhner; Yubing Tong; Shuxu Guo; Rosemary Attor; Danica Reinicke; Drew A. Torigian

doi:10.1117/12.2217672

29 March 2016 Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

Yu Liu, Jayaram K. Udupa, Dewey Odhner, Yubing Tong, Shuxu Guo, Rosemary Attor, Danica Reinicke, Drew A. Torigian

Author Affiliations +

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 97881J (2016) https://doi.org/10.1117/12.2217672
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used — optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1–3 voxels is achieved.

Citation Download Citation

Yu Liu, Jayaram K. Udupa, Dewey Odhner, Yubing Tong, Shuxu Guo, Rosemary Attor, Danica Reinicke, and Drew A. Torigian "Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881J (29 March 2016); https://doi.org/10.1117/12.2217672

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