Development of a Deep Learning Model for the Volumetric Assessment of Osteonecrosis of the Femoral Head on Three-Dimensional Magnetic Resonance Imaging.

Abstract

Although volumetric assessment of necrotic lesions using the Steinberg classification predicts future collapse in osteonecrosis of the femoral head (ONFH), quantifying these lesions using magnetic resonance imaging (MRI) generally requires time and effort, preventing the Steinberg classification from being routinely used in clinical investigations. Thus, this study aimed to use deep learning to develop a method for automatically segmenting necrotic lesions using MRI and for automatically classifying them according to the Steinberg classification. A total of 63 hips from patients who had ONFH and did not have collapse were included. An orthopaedic surgeon manually segmented the femoral head and necrotic lesions on MRI acquired using a spoiled gradient-echo sequence. Based on manual segmentation, 22 hips were classified as Steinberg grade A, 23 as Steinberg grade B, and 18 as Steinberg grade C. The manually segmented labels were used to train a deep learning model that used a 5-layer Dynamic U-Net system. A four-fold cross-validation was performed to assess segmentation accuracy using the Dice coefficient (DC) and average symmetric distance (ASD). Furthermore, hip classification accuracy according to the Steinberg classification was evaluated along with the weighted Kappa coefficient. The median DC and ASD for the femoral head region were 0.95 (interquartile range [IQR], 0.95 to 0.96) and 0.65 mm (IQR, 0.59 to 0.75), respectively. For necrotic lesions, the median DC and ASD were 0.89 (IQR, 0.85 to 0.92) and 0.76 mm (IQR, 0.58 to 0.96), respectively. Based on the Steinberg classification, the grading matched in 59 hips (accuracy: 93.7%), with a weighted Kappa coefficient of 0.98. The proposed deep learning model exhibited high accuracy in segmenting and grading necrotic lesions according to the Steinberg classification using MRI. This model can be used to assist clinicians in the volumetric assessment of ONFH.