PURPOSE: Developmental dysplasia of the hip (DDH) is a congenital deformity which in severe cases leads to hip dislocation and in milder cases to premature osteoarthritis. Image-aided diagnosis of DDH is partly based on Graf classification which quantifies the acetabular shape seen at two-dimensional ultrasound (2DUS), which leads to high inter-scan variance. 3D ultrasound (3DUS) is a promising alternative for more reliable DDH diagnosis. However, manual quantification of acetabular shape from 3DUS is cumbersome. METHODS: Here, we (1) propose a semiautomated segmentation algorithm to delineate 3D acetabular surface models from 3DUS using graph search; (2) propose a fully automated method to classify acetabular shape based on a random forest (RF) classifier using features derived from 3D acetabular surface models; and (3) test diagnostic accuracy on a dataset of 79 3DUS infant hip recordings (36 normal, 16 borderline, 27 dysplastic based on orthopedic surgeon assessment) in 42 patients. For each 3DUS, we performed semiautomated segmentation to produce 3D acetabular surface models and then calculated geometric features including the automatic [Formula: see text]lpha (AA), acetabular contact angle (ACA), kurtosis (K), skewness (S) and convexity (C). Mean values of features obtained from surface models were used as inputs to train a RF classifier. RESULTS: Surface models were generated rapidly (user time 46.2 s) via semiautomated segmentation and visually closely correlated with actual acetabular contours (RMS error 1.39 ± 0.7 mm). A paired nonparametric u test on of feature values in each category showed statistically significant variation (p < 0.001) for AA, ACA and convexity. The RF classifier was 100 % specific and 97.2 % sensitive in classifying normal versus dysplastic hips and yielded true positive rates of 94.4, 62.5 and 89.9 % for normal, borderline and dysplastic hips. CONCLUSIONS: The proposed technique reduces the subjectivity of image-aided DDH diagnosis and could be useful in clinical practice.
PURPOSE:Developmental dysplasia of the hip (DDH) is a congenital deformity which in severe cases leads to hip dislocation and in milder cases to premature osteoarthritis. Image-aided diagnosis of DDH is partly based on Graf classification which quantifies the acetabular shape seen at two-dimensional ultrasound (2DUS), which leads to high inter-scan variance. 3D ultrasound (3DUS) is a promising alternative for more reliable DDH diagnosis. However, manual quantification of acetabular shape from 3DUS is cumbersome. METHODS: Here, we (1) propose a semiautomated segmentation algorithm to delineate 3D acetabular surface models from 3DUS using graph search; (2) propose a fully automated method to classify acetabular shape based on a random forest (RF) classifier using features derived from 3D acetabular surface models; and (3) test diagnostic accuracy on a dataset of 79 3DUS infant hip recordings (36 normal, 16 borderline, 27 dysplastic based on orthopedic surgeon assessment) in 42 patients. For each 3DUS, we performed semiautomated segmentation to produce 3D acetabular surface models and then calculated geometric features including the automatic [Formula: see text]lpha (AA), acetabular contact angle (ACA), kurtosis (K), skewness (S) and convexity (C). Mean values of features obtained from surface models were used as inputs to train a RF classifier. RESULTS: Surface models were generated rapidly (user time 46.2 s) via semiautomated segmentation and visually closely correlated with actual acetabular contours (RMS error 1.39 ± 0.7 mm). A paired nonparametric u test on of feature values in each category showed statistically significant variation (p < 0.001) for AA, ACA and convexity. The RF classifier was 100 % specific and 97.2 % sensitive in classifying normal versus dysplastic hips and yielded true positive rates of 94.4, 62.5 and 89.9 % for normal, borderline and dysplastic hips. CONCLUSIONS: The proposed technique reduces the subjectivity of image-aided DDH diagnosis and could be useful in clinical practice.
Entities:
Keywords:
3DUS; DDH classification; Hip segmentation; Random forest
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