EvoSAM可持续学习，动态进化以适应多样医疗影像分割需求。
Dynamically evolving segment anything model with continuous learning for medical image segmentation
- 基于持续学习机制，模型在新任务中动态更新知识
- 在血管和前列腺MRI分割上准确率提升且避免灾难性遗忘
- 临床医生测试表明其能高效响应用户提示，适合实际诊疗场景
医学图像分割对临床诊断、手术规划和治疗监测至关重要。传统方法通常通过一次性训练应对所有分割任务，但在实际应用中，医学图像分割的任务与场景持续多样化，亟需能够动态演进的模型。本文提出EvoSAM，一种可连续学习的动态演化医疗图像分割模型，能从不断扩展的任务与场景中持续积累新知识，增强分割能力。在手术图像血管分割和多中心前列腺MRI分割上的大量评估表明，EvoSAM不仅提升了分割精度，还有效缓解了灾难性遗忘问题。临床医生在血管分割任务中的实验进一步验证，该模型能根据用户提示显著提升分割效率，展现出良好的临床应用前景。
原文摘要 · Abstract (English)
Medical image segmentation is essential for clinical diagnosis, surgical planning, and treatment monitoring. Traditional approaches typically strive to tackle all medical image segmentation scenarios via one-time learning. However, in practical applications, the diversity of scenarios and tasks in medical image segmentation continues to expand, necessitating models that can dynamically evolve to meet the demands of various segmentation tasks. Here, we introduce EvoSAM, a dynamically evolving medical image segmentation model that continuously accumulates new knowledge from an ever-expanding array of scenarios and tasks, enhancing its segmentation capabilities. Extensive evaluations on surgical image blood vessel segmentation and multi-site prostate MRI segmentation demonstrate that EvoSAM not only improves segmentation accuracy but also mitigates catastrophic forgetting. Further experiments conducted by surgical clinicians on blood vessel segmentation confirm that EvoSAM enhances segmentation efficiency based on user prompts, highlighting its potential as a promising tool for clinical applications.
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