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.

翻译：提示式分割基础模型已成为满足医学图像多样化需求的一种变革性方法，但现有模型大多需要昂贵的计算资源，这对其在临床实践中的采用构成了巨大障碍。在本研究中，我们组织了首个专注于提示式医学图像分割的国际竞赛，该竞赛采用了一个大规模数据集，涵盖来自20多家不同机构的九种常见成像模态。优胜团队开发了轻量级分割基础模型，并实现了高效推理流程，在保持最先进分割精度的同时大幅降低了计算需求。此外，赛后阶段通过设计性能提升和可复现性任务进一步改进了算法，不仅优化了算法性能，还验证了获胜方案的可复现性。更重要的是，表现最佳的算法已被集成至具有友好用户界面的开源软件中，以促进临床采用。所有数据与代码均已公开，旨在推动医学图像分割基础模型的进一步发展，并为具有实际影响力的现实应用铺平道路。