Lehmann Thomas M, Bredno Jörg
Institut für Medizinische Informatik, RWTH Aachen, Aachen D-52027, Germany.
J Am Med Inform Assoc. 2005 Sep-Oct;12(5):497-504. doi: 10.1197/jamia.M1652. Epub 2005 May 19.
Medical imaging informatics must exceed the mere development of algorithms. The discipline is also responsible for the establishment of methods in clinical practice to assist physicians and improve health care. From our point of view, it is commonly accepted that model-based analysis of medical images is superior to other concepts, but only a few applications are found in daily clinical use. The gap between development of model-based image analysis and its routine application can be addressed by identifying four necessary transfer steps: formulation, parameterization, instantiation, and validation. Usually, computer scientists formulate the model and define its parameterization, i.e., configure a model to handle a selected subset of clinical data. During instantiation, the algorithm adapts the model to the actual data, which is validated by physicians. Since medical a priori knowledge and particular knowledge on technical details are required for parameterization and validation, these steps are considered to be bottlenecks. In this paper, we propose general schemes that allow an application- or image-specific parameterization to be performed by medical users. Combining noncontextual and contextual approaches, we also suggest a reliable scheme that allows application-specific validation, even if a gold standard is unavailable. To emphasize our point of view, we provide examples based on unsupervised segmentation in medical imagery, which is one of the most difficult tasks. Following the proposed schemes, an exact delineation of cells in micrographs is parameterized, validated, and successfully established in daily clinical use, while automatic determination of body regions in radiographs cannot be configured to support reliable and robust clinical use. The results stress that parameterization and validation must be based on clinical data that show all potential variations and artifact sources.
医学影像信息学绝不能仅仅局限于算法开发。该学科还负责在临床实践中建立方法，以协助医生并改善医疗保健。从我们的角度来看，基于模型的医学图像分析通常被认为优于其他概念，但在日常临床应用中却很少见。基于模型的图像分析开发与其常规应用之间的差距可以通过识别四个必要的转化步骤来解决：公式化、参数化、实例化和验证。通常，计算机科学家制定模型并定义其参数化，即配置一个模型来处理选定的临床数据子集。在实例化过程中，算法会使模型适应实际数据，并由医生进行验证。由于参数化和验证需要医学先验知识以及关于技术细节的特定知识，因此这些步骤被视为瓶颈。在本文中，我们提出了通用方案，允许医学用户进行特定于应用或图像的参数化。结合非上下文和上下文方法，我们还提出了一种可靠的方案，即使没有金标准，也能进行特定于应用的验证。为了强调我们的观点，我们提供了基于医学图像中无监督分割的示例，这是最困难的任务之一。按照所提出的方案，显微照片中细胞的精确描绘在日常临床应用中进行了参数化、验证并成功建立，而X光片中身体区域的自动确定却无法配置以支持可靠且稳健的临床应用。结果强调，参数化和验证必须基于能够显示所有潜在变化和伪影来源的临床数据。
