Deep Learning Segmentation of Lower Extremities Radiographs for an Automatic Leg Length Discrepancy Measurement.

Abstract

Leg length measurement is relevant for the early diagnostic and treatment of discrepancies as they are related with orthopedic and biomechanical changes. Simple radiology constitutes the gold standard on which radiologists perform manual lower limb measurements. It is a simple task but represents an inefficient use of their time, expertise and knowledge that could be spent in more complex labors. In this study, a pipeline for semantic bone segmentation in lower extremities radiographs is proposed. It uses a deep learning U-net model and performs an automatic measurement without consuming physicians' time. A total of 20 radiographs were used to test the methodology proposed obtaining a high overlap between manual and automatic masks with a Dice coefficient value of 0.963. The obtained Spearman's rank correlation coefficient between manual and automatic leg length measurements is statistically different from cero except for the angle of the left mechanical axis. Furthermore, there is no case in which the proposed automatic method makes an absolute error greater than 2 cm in the quantification of leg length discrepancies, being this value the degree of discrepancy from which medical treatment is required.Clinical Relevance- Leg length discrepancy measurements from X-ray images is of vital importance for proper treatment planning. This is a laborious task for radiologists that can be accelerated using deep learning techniques.