  The ultrasound (US) screening of the infant hip is vital for the early
diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH
refers to measuring alpha and beta angles that quantify hip joint development.
These two angles are calculated from key anatomical landmarks and structures of
the hip. However, this measurement process is not trivial for sonographers and
usually requires a thorough understanding of complex anatomical structures. In
this study, we propose a multi-task framework to learn the relationships among
landmarks and structures jointly and automatically evaluate DDH. Our multi-task
networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as
the basic framework to detect and segment key anatomical structures and add one
landmark detection branch to form a new multi-task framework. Secondly, we
propose a novel shape similarity loss to refine the incomplete anatomical
structure prediction robustly and accurately. Thirdly, we further incorporate
the landmark-structure consistent prior to ensure the consistency of the bony
rim estimated from the segmented structure and the detected landmark. In our
experiments, 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 alpha
and 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 can
accurately and robustly realize the automatic evaluation of DDH, showing great
potential for clinical application.
