Ultrasound Nerve Segmentation Using RESU-NET Architecture

Abstract

Ultrasound Nerve Segmentation enhances the precision and safety of ultrasound-guided procedures by automating nerve identification using deep learning, specifically Convolutional Neural Networks (CNNs). This project employs an optimized U-Net architecture trained on labeled ultrasound datasets, with preprocessing techniques like augmentation and normalization to improve robustness. Dice Loss is used as the objective function, ensuring high segmentation accuracy, evaluated through metrics like Intersection over Union (IoU) and Dice Coefficient. Post-processing methods further refine segmentation masks for clinical reliability. By minimizing human error and improving workflow efficiency, this approach enhances patient safety and underscores the transformative role of AI in medical imaging. Future advancements include mobile deployment using TensorFlow Lite for real-time access in clinical settings and transfer learning to enhance model performance with limited datasets. The integration of automation in ultrasound imaging can revolutionize regional anesthesia and nerve block procedures, making them safer and more efficient. This study highlights the potential of AI-driven healthcare solutions, bridging technology with medicine to improve diagnostic precision and treatment outcomes.