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.
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Acknowledgements
The authors thank Laura Bollinger and Jiro Wada for their expertise in helping to prepare this manuscript. Staff from MIM Software provided guidance in use of the digital atlas building framework. The authors also thank the comparative medicine group at the NIAID Integrated Research Facility for caring and handling of NHPs.
Funding
This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This work was funded in part through Battelle Memorial Institute’s prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272200700016I. The content of this publication does not necessarily reflect the views or policies of the US Department of Health and Human Services (DHHS) or of the institutions and companies affiliated with the authors. MH, JS and RB performed this work as employees of Battelle Memorial Institute. Subcontractors to Battelle Memorial Institute who performed this work are: MC, CB, PS, and JL, all employees of MEDRelief Staffing. NHPs were provided by the Division of Microbiology and Infectious Diseases and the Office of Research Services/Division of Veterinary Resources, National Institutes of Health (NOR15003-001-0000).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Christopher Bartos, Philip Sayre, Jennifer Sword, Jeffrey Solomon, Nina Aiosa and Dara Bradley. The first draft of the manuscript was written by Jeffrey Solomon and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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The authors (Jeffrey Solomon, Nina Aiosa, Dara Bradley, Marcelo A. Castro, Syed Reza, Christopher Bartos, Philip Sayre, Ji Hyun Lee, Jennifer Sword, Michael R. Holbrook, Richard S. Bennett, Dima A. Hammoud, Reed F. Johnson, Irwin Feuerstein) declare that they have no conflicts of interest.
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The animals were housed in an Association for Assessment and Accreditation of Laboratory Care-International-accredited facility. All experimental procedures were approved by the NIAID Division of Clinical Research (DCR) Animal Care and Use Committee and were in compliance with the Animal Welfare Act regulations, Publish Health Service policy, and the Guide for the Care and Use of Laboratory Animals recommendations.
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MIM Software (Cleveland, Ohio) is commercially available.
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Solomon, J., Aiosa, N., Bradley, D. et al. Atlas-based liver segmentation for nonhuman primate research. Int J CARS 15, 1631–1638 (2020). https://doi.org/10.1007/s11548-020-02225-9
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DOI: https://doi.org/10.1007/s11548-020-02225-9