An Efficient Muscle Segmentation Method Via Bayesian Fusion of Probabilistic Shape Modeling and Deep Edge Detection

Objective:
Paraspinal muscle segmentation and reconstruction from MR images are critical to implement quantitative assessment of chronic and recurrent low back pains. Due to unclear muscle boundaries and shape variations, current segmentation methods demonstrate suboptimal performance with insufficient training samples. This work proposes a novel approach to modeling and inferring muscle shapes that enhances segmentation accuracy and efficiency with few training data.
Methods:
Firstly, a probabilistic shape model (PSM) based on Fourier basis functions and Gaussian processes (GPs) is designed to encode 3D muscle shapes, where anatomical meanings are attributed to the model's geometric parameters. Muscle shape variations and correlations are described by the GPs of the geometric parameters, which allow a small size of parameters to model the distribution of muscle shapes. Secondly, a Bayesian framework is developed to achieve entire muscle segmentation by posterior estimations. The framework fuses the geometric prior of the PSM with observations of deep-learning-based edge detections (DED) and sparse manual annotations, by which issues of unclear boundaries and shape variations can be compensated.
Results and conclusion:
Experiments on public and clinical datasets demonstrate that, with just three manually annotated slices, our method achieves a Dice similarity coefficient exceeding 90%, which outperforms other methods. Meanwhile, our method needs only a small training dataset and offers rapid inference speeds in clinical applications.
Significance:
Our study enables precise assessment of paraspinal muscles in 2D and 3D, aiding clinicians and researchers in understanding muscle changes in various conditions, potentially enhancing treatment outcomes.