利用深度学习区分癌症后和健康人舌肌在言语时的协调模式。
Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning.
机构信息
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
出版信息
J Acoust Soc Am. 2019 May;145(5):EL423. doi: 10.1121/1.5103191.
The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.
区分癌症后与健康舌肌协调模式的能力对于推进言语运动控制理论的发展和治疗及康复策略的制定是必要的。本研究提出了一种基于深度学习的方法，利用磁共振成像（MRI）的肌肉协调模式对两组进行分类。该方法使用标记 MRI 来跟踪舌部内部组织点，并采用图谱驱动的非负矩阵分解来降低变形场的维度。应用卷积神经网络进行分类任务，准确率为 96.90%，为言语相关障碍的治疗或康复策略的制定提供了可能。
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