13 March 2018 Automated location detection of injection site for preclinical stereotactic neurosurgery through fully convolutional network
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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 location error and improve repeatability of experiments and treatments, we investigate an automated method to locate injection sites. This paper proposes fully convolutional networks to locate specific anatomical points on skulls of rodents. Preliminary results show that fully convolutional networks are capable to identify and locate Bregma and Lambda points on rodent skulls. his method has the advantage of rotation and shifting invariance, and simplifies the procedure of locating injection sites. In the future study, the location error will be quantified, and the fully convolutional networks will be improved by expanding the training dataset as well as exploring other structures of convolutional networks.
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Zheng Liu, Hemmings Wu, Shiva Abbaszadeh, "Automated location detection of injection site for preclinical stereotactic neurosurgery through fully convolutional network", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057623 (13 March 2018); doi: 10.1117/12.2293715; https://doi.org/10.1117/12.2293715
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