用SAM模型精准分割胸部X光片肺部，效果媲美顶尖神经网络。
Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images
- 对SAM进行微调，提升其在胸部X光图像上的肺部分割能力。
- 调整后模型性能显著优于原始数据集标注，接近U-Net水平。
- 适合医疗影像研究者快速部署高精度分割工具。
Meta AI于2023年4月发布的Segment Anything Model（SAM）是一种强大的通用图像分割模型，通过数百万张图像与掩码训练，可实现对图像中任意对象的语义识别与分离。本研究聚焦医疗领域，探索将SAM应用于胸部X光片中的肺部分割任务。为提升模型性能，采用迁移学习中的微调策略。实验表明，经过调整后，模型在评估指标上显著优于原始数据集提供的掩码，结果达到与先进神经网络（如U-Net）相当的水平，具备临床应用潜力。
原文摘要 · Abstract (English)
Segment Anything Model (SAM), a new AI model from Meta AI released in April 2023, is an ambitious tool designed to identify and separate individual objects within a given image through semantic interpretation. The advanced capabilities of SAM are the result of its training with millions of images and masks, and a few days after its release, several researchers began testing the model on medical images to evaluate its performance in this domain. With this perspective in focus -- i.e., optimizing work in the healthcare field -- this work proposes the use of this new technology to evaluate and study chest X-ray images. The approach adopted for this work, with the aim of improving the model's performance for lung segmentation, involved a transfer learning process, specifically the fine-tuning technique. After applying this adjustment, a substantial improvement was observed in the evaluation metrics used to assess SAM's performance compared to the masks provided by the datasets. The results obtained by the model after the adjustments were satisfactory and similar to cutting-edge neural networks, such as U-Net.
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