SAM2CLIP2SAM：用于Covid-19检测的3D CT扫描分割视觉语言模型
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
AI总结：
本文提出SAM2CLIP2SAM框架，结合SAM和CLIP的优势实现3D胸部CT扫描的准确肺部分割，并将分割结果输入RACNet进行Covid-19检测，实验证明该方法提升了分类性能。
摘要：
本文提出了一种新的有效图像分割方法，可集成到任何模型和方法中；我们选择的范式是用于Covid-19检测的医学图像（3D胸部CT扫描）分类。我们的方法结合了视觉语言模型对CT扫描进行分割，然后将其输入到名为RACNet的深度神经架构中进行Covid-19检测。特别地，我们引入了一个名为SAM2CLIP2SAM的新框架进行分割，该框架利用Segment Anything Model（SAM）和Contrastive Language-Image Pre-Training（CLIP）的优势，准确分割CT扫描中的左右肺，随后将这些分割输出输入RACNet以分类COVID-19和非COVID-19病例。首先，SAM为CT扫描的每个切片生成多个基于部位的分割掩码；然后CLIP仅选择与感兴趣区域（ROIs），即左右肺相关的掩码；最后将ROIs作为提示提供给SAM，生成肺部的最终分割掩码。我们在两个Covid-19标注数据库上进行了实验，结果表明使用我们的方法进行CT扫描分割获得了性能提升。
英文摘要：
This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medical images (3-D chest CT scans) for Covid-19 detection. Our approach includes a combination of vision-language models that segment the CT scans, which are then fed to a deep neural architecture, named RACNet, for Covid-19 detection. In particular, a novel framework, named SAM2CLIP2SAM, is introduced for segmentation that leverages the strengths of both Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to accurately segment the right and left lungs in CT scans, subsequently feeding these segmented outputs into RACNet for classification of COVID-19 and non-COVID-19 cases. At first, SAM produces multiple part-based segmentation masks for each slice in the CT scan; then CLIP selects only the masks that are associated with the regions of interest (ROIs), i.e., the right and left lungs; finally SAM is given these ROIs as prompts and generates the final segmentation mask for the lungs. Experiments are presented across two Covid-19 annotated databases which illustrate the improved performance obtained when our method has been used for segmentation of the CT scans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
