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

TL;DR

This paper introduces a multi-task deep learning framework that automatically evaluates developmental dysplasia of the hip from ultrasound images by jointly learning landmarks and structures, achieving high accuracy and robustness.

Contribution

It proposes a novel multi-task network with three modules, including a shape similarity loss and landmark-structure consistency, for improved DDH assessment from ultrasound images.

Findings

Average angle errors are 2.221° (alpha) and 2.899° (beta).

93% and 85% of estimates have errors less than 5° for alpha and beta.

The method demonstrates high accuracy and robustness in automatic DDH evaluation.

Abstract

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.