论文标题
使用深度学习在侧胸部放射线照相上对椎骨的自动分割
Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning
论文作者
论文摘要
这项研究的目的是开发一种自动化算法，用于使用深度学习对胸部X线摄影进行胸椎分割。获得了124个对独特患者的侧面胸部X光片的124个识别。可见椎骨的分割由医学生手动进行，并由董事会认证的放射科医生进行验证。 74张图像用于培训，10张用于验证，40张进行了测试。使用骰子系数和二进制跨透镜作为损耗函数的总和，采用了U-NET深卷积神经网络进行分割。在测试集中，该算法的平均骰子系数值为90.5，平均跨工会（IOU）的平均骰子系数为81.75。深度学习证明了椎骨侧面射线照相的分割有望。
The purpose of this study is to develop an automated algorithm for thoracic vertebral segmentation on chest radiography using deep learning. 124 de-identified lateral chest radiographs on unique patients were obtained. Segmentations of visible vertebrae were manually performed by a medical student and verified by a board-certified radiologist. 74 images were used for training, 10 for validation, and 40 were held out for testing. A U-Net deep convolutional neural network was employed for segmentation, using the sum of dice coefficient and binary cross-entropy as the loss function. On the test set, the algorithm demonstrated an average dice coefficient value of 90.5 and an average intersection-over-union (IoU) of 81.75. Deep learning demonstrates promise in the segmentation of vertebrae on lateral chest radiography.
