对比多种模型，发现基础模型更擅长肺部肿瘤分割。
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
- 对比传统模型与基础模型在肺肿瘤分割中的表现。
- MedSAM~2 在准确率和效率上均优于其他模型。
- 适合临床医生和医学影像研究者参考使用。
精准的肺肿瘤分割对提升肿瘤诊断、治疗规划及患者预后至关重要。然而，肿瘤形态、大小和位置的复杂性给自动化分割带来巨大挑战。本研究对基于深度学习的分割模型进行了全面基准测试，比较了U-Net、DeepLabV3等传统架构，nnUNet等自配置模型，以及MedSAM、MedSAM~2等基础模型。在两个肺肿瘤分割数据集上，评估了不同学习范式（包括少样本学习和微调）下的分割精度与计算效率。结果表明，尽管传统模型在肿瘤边界划分上表现不佳，基础模型尤其是MedSAM~2，在准确率和计算效率方面均显著领先。研究证实了基础模型在肺肿瘤分割中的潜力，强调其在改善临床工作流程和患者预后方面的应用价值。
原文摘要 · Abstract (English)
Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and location poses significant challenges for automated segmentation. This study presents a comprehensive benchmarking analysis of deep learning-based segmentation models, comparing traditional architectures such as U-Net and DeepLabV3, self-configuring models like nnUNet, and foundation models like MedSAM, and MedSAM~2. Evaluating performance across two lung tumor segmentation datasets, we assess segmentation accuracy and computational efficiency under various learning paradigms, including few-shot learning and fine-tuning. The results reveal that while traditional models struggle with tumor delineation, foundation models, particularly MedSAM~2, outperform them in both accuracy and computational efficiency. These findings underscore the potential of foundation models for lung tumor segmentation, highlighting their applicability in improving clinical workflows and patient outcomes.
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