利用卷积神经网络评估髋关节发育不良的髋关节X光片不对齐情况

Sheridan Perry, Matthew Folkman, Takara O’Brien, Lauren Wilson, Eric Coyle, Raymond W. Liu, Charles T. Price, Victor Huayamave
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摘要

髋关节发育不良(DDH)是指髋臼不能充分容纳股骨头。如果不及时治疗,DDH 可导致髋关节退行性病变。有几种成像技术可用于 DDH 评估。在X光片中,髋臼指数、中心边缘角、夏普角和移位百分比指标被用于评估DDH。确定这些指标既耗时又重复。本研究使用卷积神经网络(CNN)来识别放射学测量结果,并改进识别 DDH 的传统方法。数据集包括 60 个受试者的 X 光片,沿头颅外侧轴和内侧轴旋转 25 次,共生成 1500 张图像。采用 CNN 检测算法来识别诊断 DDH 的关键放射学指标。与人工计算的指标相比,该算法能够以合理的准确度检测到这些指标。CNN 在骨与软组织之间具有高对比度边缘的图像上表现良好。相比之下,在一些因骨与软组织对比度低而清晰度较差的图像上,CNN 无法识别一些关键点进行度量计算。这项研究表明,CNN 可以有效地测量临床参数,以评估骨与软组织之间对比度边缘较高的 X 光片上的 DDH,并有目的地旋转理想图像。这项研究的结果有助于为使用 CNN 进行放射学测量和医疗状况预测提供信息,并扩大现有的信息库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip
Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head. If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index, center-edge angle, Sharp's angle, and migration percentage metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction.
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