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Automatically detecting bregma and lambda points in rodent skull anatomy images

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  • Peng Zhou
  • Zheng Liu
  • Hemmings Wu
  • Yuli Wang
  • Yong Lei
  • Shiva Abbaszadeh

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 μm. This method is robust to different lighting conditions and mouse orientations, and has the potential to simplify the procedure of locating injection sites.

Suggested Citation

  • Peng Zhou & Zheng Liu & Hemmings Wu & Yuli Wang & Yong Lei & Shiva Abbaszadeh, 2020. "Automatically detecting bregma and lambda points in rodent skull anatomy images," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0244378
    DOI: 10.1371/journal.pone.0244378
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