改进SAM模型，让医学图像分割更准确且无需大量标注。
MedicoSAM: Robust Improvement of SAM for Medical Imaging
- 用多种微调策略优化SAM，适配医学图像特点。
- 交互式分割性能显著提升，自动语义分割无明显改善。
- 模型开源可用，兼容现有标注工具，适合临床研究使用。
医学图像分割在临床和研究中至关重要。深度学习虽大幅推进该领域，但现有方法多依赖特定任务训练，新任务需大量人工标注数据，成本高昂。视觉基础模型（如Segment Anything）为通用分割提供了新路径。本文通过在大规模多样医学数据集上对比不同微调策略，评估模型在交互式与自动语义分割任务中的表现。结果表明，交互式分割性能显著提升，但语义分割未因医学图像预训练而获益。最佳模型MedicoSAM已公开于https://github.com/computational-cell-analytics/medico-sam，兼容现有标注工具，具备重要实用价值。
原文摘要 · Abstract (English)
Medical image segmentation is an important analysis task in clinical practice and research. Deep learning has massively advanced the field, but current approaches are mostly based on models trained for a specific task. Training such models or adapting them to a new condition is costly due to the need for (manually) labeled data. The emergence of vision foundation models, especially Segment Anything, offers a path to universal segmentation for medical images, overcoming these issues. Here, we study how to improve Segment Anything for medical images by comparing different finetuning strategies on a large and diverse dataset. We evaluate the finetuned models on a wide range of interactive and (automatic) semantic segmentation tasks. We find that the performance can be clearly improved for interactive segmentation. However, semantic segmentation does not benefit from pretraining on medical images. Our best model, MedicoSAM, is publicly available at https://github.com/computational-cell-analytics/medico-sam. We show that it is compatible with existing tools for data annotation and believe that it will be of great practical value.
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