Toward automatic diagnosis of hip dysplasia from 2d ultrasound

Developmental dysplasia of the hip (DDH) is a congenital deformity occurring in similar to 3% of infants. If diagnosed early most cases of DDH can be effectively treated using a Pavlik harness. However, current diagnosis of DDH using 2D ultrasound is and can have high inter-operator variability. In this paper we propose a method to automatically segment the acetabulum bone and derive geometric indices of hip dysplasia from this model. In the proposed method, using multi-scale superpixels, we incorporate global and local image features into a Deep Learning framework to obtain a probability map of the bone to be segmented and then use this map in probabilistic graph search to guide the segmentation. Clinically relevant geometric measures of hip dysplasia, including a new index of acetabular rounding, are then automatically calculated from the segmented acetabulum contour. We tested this method on 2D ultrasound of 50 infant hips and the contours generated matched closely with manual segmentations at root mean square error 1.8 +/- 0.7 mm and Hausdorff distance 2.1 +/- 0.9 mm. In this pilot data, the measured indices of dysplasia give an area under the curve of 86.2% for classifying normal vs dysplastic hips. The proposed approach could be used clinically for accurate and automatic diagnosis of hip dysplasia in infants.