A fully automated measurement of migration percentage on ultrasound images in children with cerebral palsy.

Abstract

Migration percentage (MP) is the gold standard to assess the severity of hip displacement in children with cerebral palsy, which is measured on anteroposterior hip radiographs. Recently, the ultrasound (US) method has been developed as a safe alternative imaging modality to image and monitor children's hips. However, measuring MP on US images (MP_US) is time-consuming, challenging, and user-dependent. This study aimed to develop machine learning algorithms to automatically measure MP_US and validate the algorithms with MP_Xray. A combination of signal filtering, convolution neural networks (CNNs), and UNets was applied to segment the regions of interest (ROI), detect edges or feature points, and select the desired US frames. A total of 62 hips including both coronal and transverse scans per hip were acquired, out of which 36 with applying augmentation method were utilized for training, 8 for validation, and 18 for testing. The intraclass correlation coefficient (ICC_2,1) and the mean absolute difference (MAD) between the automated MP_US versus manual MP_Xray were 0.86 and 6.5% ± 5.5%, respectively. To report the MP_US, it took an average of 104 s/hip. This preliminary result demonstrated that MP_US was able to extract automatically within 2 min with a clinical acceptance accuracy (10%).