Automated segmentation and classification of lumbar transverse ultrasound views using a two-stage deep learning method

The proposed two-stage framework, which integrates semantic segmentation with coordinate-based classification, enables accurate automated recognition of lumbar ultrasound views. By explicitly modeling key anatomical landmarks and their spatial relationships, this approach reduces reliance on operator experience and may support safer and more efficient ultrasound-guided lumbar procedures. Future work will focus on expanding the dataset and validating the generalizability of the method across diverse clinical settings.