Atlas-based liver segmentation for nonhuman primate research
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SHORT COMMUNICATION
Atlas‑based liver segmentation for nonhuman primate research Jeffrey Solomon1,3 · Nina Aiosa2 · Dara Bradley2 · Marcelo A. Castro3 · Syed Reza2 · Christopher Bartos3 · Philip Sayre3 · Ji Hyun Lee3 · Jennifer Sword3 · Michael R. Holbrook3 · Richard S. Bennett3 · Dima A. Hammoud2 · Reed F. Johnson3 · Irwin Feuerstein3 Received: 27 February 2020 / Accepted: 30 June 2020 © CARS 2020
Abstract Purpose Certain viral infectious diseases cause systemic damage and the liver is an important organ affected directly by the virus and/or the hosts’ response to the virus. Medical imaging indicates that the liver damage is heterogenous, and therefore, quantification of these changes requires analysis of the entire organ. Delineating the liver in preclinical imaging studies is a time-consuming and difficult task that would benefit from automated liver segmentation. Methods A nonhuman primate atlas-based liver segmentation method was developed to support quantitative image analysis of preclinical research. A set of 82 computed tomography (CT) scans of nonhuman primates with associated manual contours delineating the liver was generated from normal and abnormal livers. The proposed technique uses rigid and deformable registrations, a majority vote algorithm, and image post-processing operations to automate the liver segmentation process. This technique was evaluated using Dice similarity, Hausdorff distance measures, and Bland–Altman plots. Results Automated segmentation results compare favorably with manual contouring, achieving a median Dice score of 0.91. Limits of agreement from Bland–Altman plots indicate that liver changes of 3 Hounsfield units (CT) and 0.4 SUVmean (positron emission tomography) are detectable using our automated method of segmentation, which are substantially less than changes observed in the host response to these viral infectious diseases. Conclusion The proposed atlas-based liver segmentation technique is generalizable to various sizes and species of nonhuman primates and facilitates preclinical infectious disease research studies. While the image analysis software used is commercially available and facilities with funding can access the software to perform similar nonhuman primate liver quantitative analyses, the approach can be implemented in open-source frameworks as there is nothing proprietary about these methods. Keywords Atlas-based segmentation · Liver · Computed tomography · Nonhuman primate research
Introduction
* Jeffrey Solomon [email protected] 1
Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Frederick, MD, USA
2
Center for Infectious Disease Imaging, Clinical Center, Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, USA
3
Division of Clinical Research, Integrated Research Facility, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
Animal models facilitate preclinical
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