Decentralized Privacy-Preserving Federated Learning for Ultrasonic Nerve Image Segmentation

Currently, Federated Learning is a research approach where multiple parties can train a model together without sharing each other’s data to solve complex problems in machine learning. Ultrasound Nerve Segmentation is a computer vision technique that automatically identifies and segments nerve structures in ultrasound images. This technique is particularly important in medical applications where accurate localization of nerves is crucial, such as during anesthesia, nerve blocks, or surgical procedures. Ultrasound Nerve Segmentation can help doctors find nerves better during medical procedures. This could make patients feel better and have better results. Talking about surgery can make even brave patients scared because it hurts and can cause a lot of pain afterward. To reduce pain, people use drugs called narcotics, but these drugs can have bad side effects. The goal of this project is to improve pain management by using indwelling catheters that block or reduce pain at its source. These catheters reduce the need for painkillers and hasten patient healing. It is crucial to precisely identify nerve structures in ultrasound images to guarantee the exact insertion of a patient’s pain management catheter. We attempt to build an algorithm that can recognize nerve structures in a dataset of neck ultrasound images in this study. To do this, we created a U-net architecture model that will accept an image as input and forecast an image with the source of the pain highlighted as the output. Achieving this objective would improve catheter placement precision and help in the future with reduced pain.