3D MFA: An automated 3D Multi-Feature Attention based approach for spine segmentation using a multi-stage network pruning

Spine segmentation poses significant challenges due to the complex anatomical structure of the spine and the variability in imaging modalities, which often results in unclear boundaries and overlaps with surrounding tissues. In this research, a novel 3D Multi-Feature Attention (MFA) model is proposed for spine segmentation. The standard MobileNetv3 is modified by adding the RCBAM (Reverse Convolution Block Attention Module) module, and FPP (Feature Pyramid Pooling) for feature enhancement. Each modified MobileNetv3 is trained separately on axial, coronal, and sagittal views of 3D images. The features are concatenated to form a 3D feature map and given to the decoder part for spine segmentation. The results show that the 3D MFA outperforms from state-of-the-art method with DCS (dice coefficient score), and IoU (Intersection over Union) of 96.52%, and 95.84% on VerSe 2020 dataset while 94.64% and 93.69% on VerSe 2019 dataset with less computational cost.