MSFF: An automated multi-scale feature fusion deep learning model for spine fracture segmentation using MRI

Abstract

Spinal fractures pose a significant healthcare challenge, impacting patient care and clinical decisions. Accurately identifying these fractures from medical images is crucial due to their location, shape, type, and severity. Meeting these challenges often necessitates employing advanced machine learning and deep learning techniques. This study introduces a novel deep learning model called MSFF (Multi-Scale Feature Fusion) specifically designed for automated spine fracture segmentation using MRI images. The MSFF architecture comprises five key modules: the Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules collectively facilitate multi-scale feature fusion, spatial feature extraction, channel-wise feature enhancement, segmentation border refinement, and focused attention on the region of interest. Subsequently, a decoder network is employed to predict fractured spine areas. Experimental findings demonstrate that the proposed MSFF approach attains superior accuracy in addressing the challenges and outperforms existing segmentation methods.