计算机科学
椎体压缩性骨折
压缩(物理)
卷积神经网络
断裂(地质)
人工智能
矢状面
骨质疏松症
数据压缩
模式识别(心理学)
放射科
医学
材料科学
内分泌学
复合材料
作者
Amir Bar,Lior Wolf,Orna Bergman Amitai,Eyal Toledano,Eldad Elnekave
摘要
The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.
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