Automatic Evaluation of Scan Adequacy and Dysplasia Metrics in 2-D Ultrasound Images of the Neonatal Hip.

Ultrasound (US) imaging of an infant's hip joint is widely used for early detection of developmental dysplasia of the hip. In current US-based diagnosis of developmental dysplasia of the hip, trained clinicians acquire US images and, if they judge them to be adequate (i.e., to contain relevant hip joint structures), analyze them manually to extract clinically useful dysplasia metrics. However, both the scan adequacy classification and dysplasia metrics extraction steps exhibit significant variability within and between both clinicians and institutions, which can result in significant over- and undertreatment rates. To reduce the subjectivity resulting from this variability, we propose a computational image analysis technique that automatically identifies adequate images and subsequently extracts dysplasia metrics from these 2-D US images. Our automatic method uses local phase symmetry-based image measures to robustly identify intensity-invariant geometric features of bone/cartilage boundaries from the US images. Using the extracted geometric features, we trained a random forest classifier to classify images as adequate or inadequate, and in the adequate images we used a subset of the geometric features to calculate key dysplasia metrics. We validated our method on a data set of 693 US scans collected from 35 infants. Our approach produces excellent agreement with clinician adequacy classifications (area under the receiver operating characteristic curve = 0.985) and in reducing variability in the measured developmental dysplasia of the hip metrics (p < 0.05). The automatically computed dysplasia metrics appear to be slightly biased toward higher Graf categories than the manually estimated metrics, which could potentially reduce missed early diagnoses.