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  • Method for determining the curved line of the cranial suture based on convolutional neural networks

    The implantation of electrodes on the skull of mice requires precise positioning. For these purposes, it is necessary to create a coordinate system that rests on the cranial sutures. The aim of the work is to train a model based on convolutional neural networks. The model should automatically draw the seam line. Investigating this issue, a method for solving the problem of seam approximation based on U-net is proposed. This study faces the difficult problem of accurately locating the cranial suture. As part of the study, a structure similar to U-net was developed and thought out. The results of the work will allow biologists to perform more accurate stereotaxic operations, by inserting electrodes into a more precise position, to receive certain signals from the brain, in order to record changes when various aromas are applied to the mouse receptors.

    Keywords: convolutional neural network (CNN), seam approximation, fully convolutional network (FCN), deep learning, semantic segmentation, object segmentation, neural network, computer vision, biomedical image, machine learning, image processing