Silhouette-based markerless motion estimation of awake rodents in PET
Access status:
Open Access
Type
Conference paperAbstract
The ability to image the brain of a freely moving rodent using motion-compensated PET presents many exciting possibilities for exploring the links between brain function and behavior. Markerless optical approaches for pose estimation have several potential advantages over marker-based ...
See moreThe ability to image the brain of a freely moving rodent using motion-compensated PET presents many exciting possibilities for exploring the links between brain function and behavior. Markerless optical approaches for pose estimation have several potential advantages over marker-based methods: improved accuracy and increased range of detectable motion; no `decoupling' of marker and head motion; and no acclimatization of the animals to attached markers. Our aim in this work was to describe and validate a silhouette-based multi-camera method for estimating the pose of a rat. Random-walk and K-means clustering approaches were very adaptable to uneven lighting and generally provided excellent object segmentations. In obtaining a high quality rat model, shape-from-silhouette and laser scanning both resulted in useful models; laser scanning provided sub-millimeter resolution with very few artifacts and was the method of choice. In our experimental validation, the 3D-2D (model-silhouette) optimization clearly converged to sub-degree and sub-millimeter alignment of the measured and estimated silhouettes. The average discrepancy between test points transformed using the estimated versus ground-truth poses was 0.94 mm ± 0.51 mm. This investigation focused on rigid motion of a rat phantom as a proof-of-principle of the technique. Future work will focus on investigating the potential of designing a non-rigid rodent body model in order to apply the method to a freely moving animal during PET imaging.
See less
See moreThe ability to image the brain of a freely moving rodent using motion-compensated PET presents many exciting possibilities for exploring the links between brain function and behavior. Markerless optical approaches for pose estimation have several potential advantages over marker-based methods: improved accuracy and increased range of detectable motion; no `decoupling' of marker and head motion; and no acclimatization of the animals to attached markers. Our aim in this work was to describe and validate a silhouette-based multi-camera method for estimating the pose of a rat. Random-walk and K-means clustering approaches were very adaptable to uneven lighting and generally provided excellent object segmentations. In obtaining a high quality rat model, shape-from-silhouette and laser scanning both resulted in useful models; laser scanning provided sub-millimeter resolution with very few artifacts and was the method of choice. In our experimental validation, the 3D-2D (model-silhouette) optimization clearly converged to sub-degree and sub-millimeter alignment of the measured and estimated silhouettes. The average discrepancy between test points transformed using the estimated versus ground-truth poses was 0.94 mm ± 0.51 mm. This investigation focused on rigid motion of a rat phantom as a proof-of-principle of the technique. Future work will focus on investigating the potential of designing a non-rigid rodent body model in order to apply the method to a freely moving animal during PET imaging.
See less
Date
2017Source title
2017 IEEE Nuclear Science Symposium and Medical Imaging ConferencePublisher
IEEEFunding information
ARC DE160100745Licence
Copyright All Rights ReservedRights statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Faculty/School
Faculty of EngineeringSchool of Aerospace, Mechanical and Mechatronic Engineering
Faculty of Medicine and Health, Brain and Mind Centre
Share