Automated computation of radiographic parameters of distal radial metaphyseal fractures in forearm X-rays

Abstract

Purpose: Radiographic parameters (RPs) provide objective support for effective decision making in determining clinical treatment of distal radius fractures (DRFs). This paper presents a novel automatic RP computation pipeline for computing the six anatomical RPs associated with DRFs in anteroposterior (AP) and lateral (LAT) forearm radiographs.

Methods: The pipeline consists of: (1) segmentation of the distal radius and ulna bones with six 2D Dynamic U-Net deep learning models; (2) landmark points detection and distal radius axis computation from the segmentations with geometric methods; (3) RP computation and generation of a quantitative DRF report and composite AP and LAT radiograph images. This hybrid approach combines the advantages of deep learning and model-based methods.

Results: The pipeline was evaluated on 90 AP and 93 LAT radiographs for which ground truth distal radius and ulna segmentations and RP landmarks were manually obtained by expert clinicians. It achieves an accuracy of 94 and 86% on the AP and LAT RPs, within the observer variability, and an RP measurement difference of 1.4 ± 1.2° for the radial angle, 0.5 ± 0.6 mm for the radial length, 0.9 ± 0.7 mm for the radial shift, 0.7 ± 0.5 mm for the ulnar variance, 2.9 ± 3.3° for the palmar tilt and 1.2 ± 1.0 mm for the dorsal shift.

Conclusion: Our pipeline is the first fully automatic method that accurately and robustly computes the RPs for a wide variety of clinical forearm radiographs from different sources, hand orientations, with and without cast. The computed accurate and reliable RF measurements may support fracture severity assessment and clinical management.

Keywords: Deep learning; Distal radius fracture; Radiographic parameters; Surgical decision support; X-ray images.