Rubin D L, Napel S
Richard M. Lucas Center, 1201 Welch Road, Office P285, Stanford, CA, USA.
Yearb Med Inform. 2010:34-42.
To identify challenges and opportunities in imaging informatics that can lead to the use of images for discovery, and that can potentially improve the diagnostic accuracy of imaging professionals.
Recent articles on imaging informatics and related articles from PubMed were reviewed and analyzed. Some new developments and challenges that recent research in imaging informatics will meet are identified and discussed.
While much literature continues to be devoted to traditional imaging informatics topics of image processing, visualization, and computerized detection, three new trends are emerging: (1) development of ontologies to describe radiology reports and images, (2) structured reporting and image annotation methods to make image semantics explicit and machine-accessible, and (3) applications that use semantic image information for decision support to improve radiologist interpretation performance. The informatics methods being developed have similarities and synergies with recent work in the biomedical informatics community that leverage large high-throughput data sets, and future research in imaging informatics will build on these advances to enable discovery by mining large image databases.
Imaging informatics is beginning to develop and apply knowledge representation and analysis methods to image datasets. This type of work, already commonplace in biomedical research with large scale molecular and clinical datasets, will lead to new ways for computers to work with image data. The new advances hold promise for integrating imaging with the rest of the patient record as well as molecular data, for new data-driven discoveries in imaging analogous to that in bioinformatics, and for improved quality of radiology practice.
识别影像信息学中的挑战与机遇，这些挑战与机遇可促使影像用于发现，并有可能提高影像专业人员的诊断准确性。
对近期关于影像信息学的文章以及来自PubMed的相关文章进行了综述和分析。识别并讨论了影像信息学近期研究将会面临的一些新进展和挑战。
虽然仍有大量文献致力于图像处理、可视化和计算机化检测等传统影像信息学主题，但出现了三个新趋势：（1）开发用于描述放射学报告和图像的本体；（2）结构化报告和图像注释方法，以使图像语义明确且机器可访问；（3）使用语义图像信息进行决策支持以提高放射科医生解读性能的应用。正在开发的信息学方法与生物医学信息学领域近期利用大型高通量数据集的工作具有相似性和协同作用，影像信息学的未来研究将基于这些进展，通过挖掘大型图像数据库来实现发现。
影像信息学开始针对图像数据集开发并应用知识表示和分析方法。这类工作在处理大规模分子和临床数据集的生物医学研究中已很常见，它将为计算机处理图像数据带来新方法。这些新进展有望将影像与患者记录的其他部分以及分子数据整合起来，有望在影像领域实现类似于生物信息学中的新的数据驱动型发现，并有望提高放射学实践的质量。
