标题： 利用 Segment Anything Model (SAM) 进行胸片中肺部分割 显示英文标题

标题： Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images

摘要： 图像分割模型（SAM），由Meta AI于2023年4月发布，是一种雄心勃勃的工具，旨在通过语义解释识别和分离给定图像中的单个对象。SAM的先进功能得益于其对数百万张图像和掩码的训练，在发布几天后，几位研究人员开始在医学图像上测试该模型以评估其在该领域的性能。基于此视角——即优化医疗保健领域的研究工作——本研究提出了使用这项新技术来评估和研究胸部X光片图像。为了提高模型在肺部分割方面的性能，所采用的方法涉及迁移学习过程，特别是微调技术。应用这一调整后，在用于评估SAM性能的指标与数据集提供的掩码之间观察到了显著改善。经过调整后，模型获得的结果令人满意，并且与先进的神经网络（如U-Net）相当。 显示英文摘要

摘要： 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.