Using deep learning and image processing technology, a standardized automatic quantitative analysis systerm of lumbar disc degeneration based on T2MRI is proposed to help doctors evaluate the prognosis of intervertebral disc (IVD) degeneration.

A semantic segmentation network BianqueNet with self-attention mechanism skip connection module and deep feature extraction module is proposed to achieve high-precision segmentation of intervertebral disc related areas. A quantitative method is proposed to calculate the signal intensity difference (SI) in IVD, average disc height (DH), disc height index (DHI), and disc height-to-diameter ratio (DHR). According to the correlation analysis results of the degeneration characteristic parameters of IVDs, 1051 MRI images from four hospitals were collected to establish the quantitative ranges for these IVD parameters in larger population around China.

The average dice coefficients of the proposed segmentation network for vertebral bodies and intervertebral discs are 97.04% and 94.76%, respectively. The designed parameters of intervertebral disc degeneration have a significant negative correlation with the Modified Pfirrmann Grade. This procedure is suitable for different MRI centers and different resolution of lumbar spine T2MRI (ICC=.874~.958). Among them, the standard of intervertebral disc signal intensity degeneration has excellent reliability according to the modified Pfirrmann Grade (macroF1=90.63%~92.02%).

we developed a fully automated deep learning-based lumbar spine segmentation network, which demonstrated strong versatility and high reliability to assist residents on IVD degeneration grading by means of IVD degeneration quantitation.

Objectives

Little biomechanical information is available about kinematically aligned (KA) total knee arthroplasty (TKA). The purpose of this study was to simulate the kinematics and kinetics after KA TKA and mechanically aligned (MA) TKA with four different limb alignments.