StrokeSeg2：临床研究工作流程中的中风病变分割
StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows
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中文总结 AI 辅助
研究针对深度学习框架在临床研究环境部署难的问题，提出 StrokeSeg2 框架。通过知识蒸馏和推理优化提升效率，确定最佳参数模型，将优化打包成多系统独立安装程序，便于为临床研究管道部署高性能分割工作流程。
中文摘要 AI 辅助
深度学习框架如 nnU-Net 在脑病变分割方面性能先进，但因软件依赖和计算需求等难以在临床研究环境部署。本文介绍了 StrokeSeg2，这是一个轻量级、模块化、跨平台的 C++/Qt 框架，旨在将资源密集型 3D 中风分割管道转化为便携且可重现的应用程序。通过知识蒸馏进行架构压缩以及使用 ONNX Runtime 与 Float16 量化进行推理优化，研究了其综合效果。在不同硬件配置下，架构蒸馏是效率提升的主要因素，能耗降低超 90%，推理时间平均减少 84%。确定了 0.84M 参数的学生模型为最佳折衷方案，在保留强大病变定位和有竞争力分割性能的同时，将原始 102.3M 参数的教师架构磁盘占用降至 2.1MB。最后，StrokeSeg2 将这些优化打包成适用于 Windows、macOS 和 Linux 的独立安装程序，便于为常规临床研究管道部署高性能分割工作流程。
英文摘要
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.
