MAESTRO: Supporting Real-Time Segmentation Networks on Surgical Systems via Edge Computing

Lin Guo, Andrea Pinto, Francesco Fiorella, Alessio Sacco, Mohammad Mahmoud, Flavio Esposito

Keywords: Machine Learning, Split Learning, Segmentation, Edge Computing

TL;DR: We introduced MAESTRO, an edge computing architecture designed to enable real-time, deep learning-based multi-organ segmentation in surgical systems.

Abstract:

Deep neural networks (DNNs) enable accurate segmentation of surgical video streams,
but their high computational and memory demands pose challenges for deployment on
resource-constrained surgical systems. We present MAESTRO, an adaptive edge comput-
ing architecture that supports real-time execution of segmentation networks on surgical
platforms. MAESTRO uses split learning to partition inference between the surgical de-
vice and an edge server, dynamically selecting the optimal cut layer to balance latency,
energy consumption, and data privacy. We evaluate MAESTRO using a YOLOv11 model
trained on the Dresden Surgical Anatomy Dataset (DSAD) and tested on Da Vinci robotic
surgery videos. Experiments demonstrate up to 43% latency reduction and 56% energy
savings compared to full offloading, while maintaining low data leakage risk. MAESTRO
provides a flexible and efficient solution for deploying segmentation networks in real-time,
privacy-sensitive surgical environments, and generalizes to other low-resource applications.

Submission Number: 103