Automated Robust Muscle Segmentation in Multi-level Contexts Using a Probabilistic Inference Framework

The paraspinal muscles are crucial for spinal stability, which can be quantitatively analyzed through image segmentation. However, unclear muscle boundaries, severe deformations, and limited training data impose great challenges for existing automatic segmentation methods. This study proposes an automated probabilistic inference framework to reconstruct 3D muscle shapes from thick-slice MRI robustly. Leveraging Fourier basis functions and Gaussian processes, we construct anatomically interpretable shape models. Multi-level contextual observations such as global poses of muscle centroids and local edges are then integrated into posterior estimation to enhance shape model initialization and optimization. The proposed framework is characterized by its intuitive representations and smooth generation capabilities, demonstrating higher accuracy in validation on both public and clinical datasets compared to state-of-the-art methods. The outcomes can aid clinicians and researchers in understanding muscle changes in various conditions, potentially enhancing diagnoses and treatments.