使用深度神经网络自动检测鼻腔和鼻窦

C. O. Laura, Patrick Hofmann, K. Drechsler, S. Wesarg
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引用次数: 8

摘要

鼻腔和副鼻窦表现出很大的患者间差异。其他情况,例如,耳甲大疱或鼻中隔偏差使其分割复杂化。与身体的其他部位一样,先前的多结构检测可以促进分割任务。本文提出了一种单独检测所有鼻窦和鼻腔的方法。为了更好地划分它们的边界,建议使用不规则多面体。为了准确预测,使用了Darknet-19深度神经网络,该网络与You Only Look Once方法相结合,在计算机视觉的其他领域显示出非常有希望的结果。57个CT扫描可用,其中85%用于训练,其余15%用于验证。
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Automatic Detection of the Nasal Cavities and Paranasal Sinuses Using Deep Neural Networks
The nasal cavity and paranasal sinuses present large interpa-tient variabilities. Additional circumstances like for example, concha bullosa or nasal septum deviations complicate their segmentation. As in other areas of the body a previous multi-structure detection could facilitate the segmentation task. In this paper an approach is proposed to individually detect all sinuses and the nasal cavity. For a better delimitation of their borders the use of an irregular polyhedron is proposed. For an accurate prediction the Darknet-19 deep neural network is used which combined with the You Only Look Once method has shown very promising results in other fields of computer vision. 57 CT scans were available of which 85% were used for training and the remaining 15% for validation.
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