Strategies to Configure Image Analysis Algorithms for Clinical Usage

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.