A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues
Source abstract
Abstract
In this paper, we propose a novel noniterative algorithm to simultaneously estimate optimal rigid transformations for serial section images, which is a key component in performing volume reconstructions of serial sections of biological tissue. To avoid the error accumulation and propagation caused by current algorithms, we add an extra condition: that the positions of the first and last section images should remain unchanged. This constrained simultaneous registration problem has not previously been solved. Our solution is noniterative; thus, it can simultaneously compute rigid transformations for a large number of serial section images in a short time. We demonstrate that our algorithm obtains optimal solutions under ideal conditions and shows great robustness under nonideal circumstances. Further, we experimentally show that our algorithm outperforms state-of-the-art methods in terms of speed and accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Medical Image Segmentation Techniques · Medical Imaging Techniques and Applications · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
The Center for Excellence in Brain Science and Intelligence Technology, CAS, Shanghai 200031, China
National Laboratory of Pattern Recognition, CASIA, Beijing 100190, China
School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
Funding
National Science Foundation of China · Special Program of Beijing Municipal Science and Technology Commission · Strategic Priority Research Program of Chinese Academy of Science · Bureau of International Cooperation, CAS
References (24)
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