Development of a Fully Automated Graf Standard Plane and Angle Evaluation Method for Infant Hip Ultrasound Scans

Abstract

Background: Graf’s method is currently the most commonly used ultrasound-based technique for the diagnosis of developmental dysplasia of the hip (DDH). However, the efficiency and accuracy of diagnosis are highly affected by the sonographers’ qualification and the time and effort expended, which has a significant intra- and inter-observer variability. Methods: Aiming to minimize the manual intervention in the diagnosis process, we developed a deep learning-based computer-aided framework for the DDH diagnosis, which can perform fully automated standard plane detection and angle measurement for Graf type I and type II hips. The proposed framework is composed of three modules: an anatomical structure detection module, a standard plane scoring module, and an angle measurement module. This framework can be applied to two common clinical scenarios. The first is the static mode, measurement and classification are performed directly based on the given standard plane. The second is the dynamic mode, where a standard plane from ultrasound video is first determined, and measurement and classification are then completed. To the best of our knowledge, our proposed framework is the first CAD method that can automatically perform the entire measurement process of Graf’s method. Results: In our experiments, 1051 US images and 289 US videos of Graf type I and type II hips were used to evaluate the performance of the proposed framework. In static mode, the mean absolute error of α, β angles are 1.71° and 2.40°, and the classification accuracy is 94.71%. In dynamic mode, the mean absolute error of α, β angles are 1.97° and 2.53°, the classification accuracy is 89.51%, and the running speed is 31 fps. Conclusions: Experimental results demonstrate that our fully automated framework can accurately perform standard plane detection and angle measurement of an infant’s hip at a fast speed, showing great potential for clinical application.

A pilot study to assess justification of radiographic evaluation for developmental dysplasia of the hip.

“…all have shown ultrasound to be an acceptable alternative to radiography evaen at the age six months as well. [9][10][11][12] There is further need for epidemiological studies to better estimate the incidence and prevalence of DDH in our population. There is also need for creating awareness regarding types of radiological modalities among all physicians to ensure early and accurate detection of DDH while simultaneously avoiding unjustified radiation exposure.…”

Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network

“…Different from one-stage methods, Mask R-CNN provides exact borders of an object rather than a rectangular bounding box that preserves a certain gap between the object and the ROI. [ 8 ] Mask R-CNN is a two-stage approach that is more accurate but slower due to its complexity and resulting computational cost. [ 10 , 11 ]…”

“…Recently, Chen et al[ 8 ] proposed the YOLOv3-tiny method to automatically segment 2D images statically and dynamically and a scoring system to evaluate the success of the method in finding the measurable images in type 1 and type 2 hips. They used a semantic segmentation approach and reported 1.71° deviation of alpha and 2.40° deviation of beta angles.…”

What’s New in Pediatric Orthopaedics

“…One study developed deep transfer learning models to screen for and diagnose hip dysplasia on radiographs 23 . Another experimental automated framework accurately detected standard planes and performed angular measurements of ultrasound images of infant hips 24 .…”