论文标题
基于卷积神经网络基于腰椎的自动分割和标记
Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray
论文作者
论文摘要
这项研究的目的是研究在730个手动注释的侧面腰椎X射线上训练的不同分割网络的分割精度。将实例分割网络与语义分割网络进行比较。该研究队列包括患病的刺和术后图像，并带有金属植入物。对于最佳性能实例分割模型，平均平均准确性和平均值相交（IOU）高达3％，平均像素精度和加权效果稍好，以获得最佳性能的语义分割模型。此外，实例分割模型的推论更容易实施，以在临床决策支持中进一步处理管道。
The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks. The study cohort comprised diseased spines and postoperative images with metallic implants. The average mean accuracy and mean intersection over union (IoU) was up to 3 percent better for the best performing instance segmentation model, the average pixel accuracy and weighted IoU were slightly better for the best performing semantic segmentation model. Moreover, the inferences of the instance segmentation models are easier to implement for further processing pipelines in clinical decision support.
