轻量化医学图像分割模型，笔记本也能跑，精度不降。
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
- 设计轻量级模型与高效推理流程，降低算力需求。
- 在九种影像模态上保持顶尖分割精度，支持临床部署。
- 开源工具链+竞赛数据集，助力医疗影像落地应用。
可提示的医学图像分割基础模型为多样化临床需求带来变革，但现有模型普遍依赖昂贵算力，阻碍其在临床中的应用。本文组织了首个专注于可提示医学图像分割的国际竞赛，构建了涵盖九种常见成像模态、来自20多家机构的大规模数据集。顶尖团队开发出轻量级分割基础模型，并实现高效推理流程，显著降低计算开销，同时保持最先进分割精度。后续阶段通过性能增强与可复现性任务进一步优化算法，验证了优胜方案的稳定性。最佳算法已集成至开源软件并提供友好界面，促进临床应用。数据与代码公开，推动医学图像分割模型持续发展，为真实世界应用铺平道路。
原文摘要 · Abstract (English)
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications.
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