StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows

2026-07-22

AI Summary

This study addresses the clinical deployment challenges of existing deep learning models for stroke lesion segmentation, which are often hindered by high computational complexity and resource demands. To overcome these limitations, the authors propose a lightweight, cross-platform C++/Qt framework that leverages knowledge distillation to compress the nnU-Net architecture, further enhanced by Float16 quantization and accelerated inference via ONNX Runtime. The resulting self-contained application supports both graphical and command-line interfaces and runs natively on Windows, macOS, and Linux without requiring Docker. The compressed model reduces parameters from 102.3M to 0.84M, occupies only 2.1MB on disk, achieves an average 84% reduction in inference time, and cuts energy consumption by over 90%, substantially improving clinical practicality.

Abstract

Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research environments due to, among other reasons, software dependencies and computational requirements. We introduce StrokeSeg2, a lightweight, modular, cross-platform C++/Qt framework designed to adapt resource-intensive 3D stroke segmentation pipelines into portable and reproducible applications. To improve compatibility with standard clinical workstations, we investigate the combined effect of architectural compression through knowledge distillation and inference optimisation using ONNX Runtime with Float16 quantisation. Across heterogeneous hardware configurations (CPU, integrated GPU, and dedicated GPU) architectural distillation emerged as the primary contributor to efficiency gains, contributing to over 90% reduction in energy consumption and an average 84% reduction in inference time. Specifically, we identify a 0.84M-parameter student model as the most favourable trade-off, reducing the original 102.3M-parameter teacher architecture to a 2.1 MB disk footprint while preserving robust lesion localisation and competitive segmentation performance. This small footprint supports the development of a self-contained installer for clinical workstation targets. Finally, StrokeSeg2 packages these optimisations into standalone installers for Windows, macOS, and Linux. By providing both graphical and commandline interfaces without Docker or external environment dependencies, StrokeSeg2 facilitates deployment of high-performance segmentation workflows for routine clinical research pipelines.

Problem

• stroke lesion segmentation
• clinical deployment
• model compression
• cross-platform framework
• resource-constrained environments

Innovation

• knowledge distillation
• model compression
• ONNX Runtime
• cross-platform deployment
• stroke lesion segmentation