Automatic quantification of morphology on magnetic resonance images of the proximal tibia

Abstract

The morphological quantification of the proximal tibia of the knee joint is important in knee replacement. Accurate knowledge of these parameters provides the basis for design of the tibial prosthesis and its fixation. Ideally, a prosthesis that is suitable for the morphological characteristics of Chinese knees is needed. In this paper, a deep learning automatic network framework is designed to achieve automatic segmentation and automatic quantitative analysis of magnetic resonance images of the tibia. An enhanced feature fusion network structure is designed, including high and low-level feature fusion path modules to create accurate segmentation of the tibia. A new method of extracting feature points and lines from outline contours of the proximal tibia is designed to automatically calculate six clinical morphological linear parameters of the tibia in real-time. The final result is an automatic visualisation of the tibial contour and automated extraction of tibial morphometric parameters. Validation of the results from our system against a gold standard obtained by manual processing by expert clinicians showed the Dice coefficient to be 0.97, the accuracy to be 0.98, and the correlation coefficients for all six morphological parameters of the automatic quantification of the tibia are above 0.96. The gender-specific study found that the values of the proximal tibial linear parameters of internal and external tibial diameter, anterior and posterior diameter, lateral plateau length, lateral plateau width, medial plateau length, and medial plateau width in male patients are significantly greater than in female patients (all P values ​< ​0.01). The results enrich the use of deep learning in medicine, providing orthopaedic specialists with a valuable and intelligent quantitative tool that can assess the progression and changes in osteoarthritis of the knee joint.