CNN-Based Nerve Block Segmentation

Effective pain management is crucial in postsurgical care, with nerve blocks emerging as a promising alternative to traditional narcotic-based treatments. This paper investigates the application of convolutional neural networks (CNNs) for the segmentation of the brachial plexus in ultrasound images, which is essential for precise catheter insertion in nerve blocks. I compare the performance of a U-Net CNN, known for its efficacy in medical image segmentation, with a traditional CNN architecture in identifying and segmenting the brachial plexus. The study utilizes a dataset of annotated ultrasound images from a Kaggle competition, comprising 5635 labeled examples. By evaluating the accuracy and efficiency of these networks, my research aims to enhance the reliability of nerve block procedures and reduce reliance on narcotics, ultimately improving patient outcomes in pain management.