上海交通大学与上海人工智能实验室联合团队近日发布了一款名为SAT（Segment Anything in radiology scans, driven by Text prompts）的3D医学图像分割大模型，这一创新突破在医学图像领域引起了广泛关注。SAT模型在3D医学图像，包括CT、MRI和PET扫描上，能够基于文本提示对497种不同的器官和病灶进行通用分割，展现了其在医学图像处理上的强大能力。
在传统的医学图像分割研究中，每个特定任务都需要专门训练模型，这不仅耗时耗力，而且限制了模型的应用范围，难以适应多样化的医疗需求。而SAT模型的出现，打破了这一局限，通过将语言理解和定位能力相结合，实现了对复杂多样的医学图像进行高效分割，为临床诊断、手术规划和疾病监测等提供了更广泛的支持。
这一研究成果的发布，标志着在推动通用医疗人工智能发展的道路上，构建一个能将语言与定位能力有效连接的医学分割工具已经成为可能。此外，SAT模型的开源特性，意味着全球的科研机构和医疗机构都能够免费访问其数据、代码和模型，促进医学图像分割技术的进一步发展和应用。
SAT模型的发布不仅体现了上海交通大学和上海人工智能实验室在科技创新领域的深厚实力，也为全球医学图像处理技术的发展注入了新的活力。随着更多研究团队的加入和合作，未来在医学图像分割领域，我们有理由期待更多令人振奋的创新成果。
有兴趣的读者可以访问以下链接获取更多详细信息：
– 论文链接：https://arxiv.org/abs/2312.17183
– 代码链接：https://github.com/zhaoziheng/SAT
– 数据链接：https://github.com/zhaoziheng/SAT-DS
英语如下：
News Title: “Shanghai Jiao Tong University Team Unveils the Global Most Comprehensive 3D Medical Big Model SAT, Supports 497 Types of Organ Segmentation”
Keywords: 3D Medical Big Model, SAT Release, Open Source
News Content: A joint team from Shanghai Jiao Tong University and the Shanghai Artificial Intelligence Laboratory recently released a 3D medical image segmentation big model named SAT (Segment Anything in radiology scans, driven by Text prompts), which has sparked significant attention in the medical imaging field. The SAT model, when applied to 3D medical images including CT, MRI, and PET scans, can universally segment 497 different organs and lesions based on text prompts, demonstrating its powerful capabilities in medical image processing.
In traditional medical image segmentation research, each specific task required dedicated model training, which was both time-consuming and labor-intensive, limiting the model’s application scope and making it difficult to meet diverse medical needs. The advent of the SAT model has overcome this limitation by combining language understanding and location capabilities, enabling efficient segmentation of complex and diverse medical images. This has provided broader support for clinical diagnosis, surgical planning, and disease monitoring.
The release of this research outcome marks a significant step towards the development of a medical segmentation tool that can effectively connect language and location capabilities for universal medical AI. Moreover, the open-source nature of the SAT model means that global research institutions and medical institutions can freely access its data, code, and model, promoting further development and application of medical image segmentation technology.
The release of the SAT model not only reflects the strong innovation capabilities of Shanghai Jiao Tong University and the Shanghai Artificial Intelligence Laboratory in the field of scientific and technological innovation, but also infuses new vitality into the development of medical image processing technology worldwide. As more research teams join and collaborate, we have reason to look forward to more exciting innovation results in the field of medical image segmentation.
For interested readers who wish to learn more, please visit the following links:
– Paper Link:
– Code Link:
– Data Link:
【来源】https://www.jiqizhixin.com/articles/2024-07-09-2
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