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Automatically detecting bregma and lambda points in rodent skull anatomy images.

ABSTRACT: Currently, injection sites of probes, cannula, and optic fibers in stereotactic neurosurgery are typically located manually. This step involves location estimations based on human experiences and thus introduces errors. In order to reduce localization error and improve repeatability of experiments and treatments, we investigate an automated method to locate injection sites. This paper proposes a localization framework, which integrates a region-based convolutional network and a fully convolutional network, to locate specific anatomical points on skulls of rodents. Experiment results show that the proposed localization framework is capable of identifying and locatin bregma and lambda in rodent skull anatomy images with mean errors less than 300 ?m. This method is robust to different lighting conditions and mouse orientations, and has the potential to simplify the procedure of locating injection sites.

SUBMITTER: Zhou P

PROVIDER: S-EPMC7771702 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Automatically detecting bregma and lambda points in rodent skull anatomy images.

Zhou Peng P Liu Zheng Z Wu Hemmings H Wang Yuli Y Lei Yong Y Abbaszadeh Shiva S

PloS one 20201229 12

Currently, injection sites of probes, cannula, and optic fibers in stereotactic neurosurgery are typically located manually. This step involves location estimations based on human experiences and thus introduces errors. In order to reduce localization error and improve repeatability of experiments and treatments, we investigate an automated method to locate injection sites. This paper proposes a localization framework, which integrates a region-based convolutional network and a fully convolution ...[more]

PMID: 33373400

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