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The ultrasound (US) screening of the infant hip is vital for the earlydiagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDHrefers to measuring alpha and beta angles that quantify hip joint development.These two angles are calculated from key anatomical landmarks and structures ofthe hip. However, this measurement process is not trivial for sonographers andusually requires a thorough understanding of complex anatomical structures. Inthis study, we propose a multi-task framework to learn the relationships amonglandmarks and structures jointly and automatically evaluate DDH. Our multi-tasknetworks are equipped with three novel modules. Firstly, we adopt Mask R-CNN asthe basic framework to detect and segment key anatomical structures and add onelandmark detection branch to form a new multi-task framework. Secondly, wepropose a novel shape similarity loss to refine the incomplete anatomicalstructure prediction robustly and accurately. Thirdly, we further incorporatethe landmark-structure consistent prior to ensure the consistency of the bonyrim estimated from the segmented structure and the detected landmark. In ourexperiments, 1,231 US images of the infant hip from 632 patients are collected,of which 247 images from 126 patients are tested. The average errors in alphaand beta angles are 2.221 degrees and 2.899 degrees. About 93% and 85%estimates of alpha and beta angles have errors less than 5 degrees,respectively. Experimental results demonstrate that the proposed method canaccurately and robustly realize the automatic evaluation of DDH, showing greatpotential for clinical application.

Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip