Visual analytics for model-based medical image segmentation: Opportunities and challenges

Segmentation of medical images is a prerequisite in clinical practice. Many segmentation algorithms use statistical shape models. Due to the lack of tools providing prior information on the data, standard models are frequently used. However, they do not necessarily describe the data in an optimal way. Model-based segmentation can be supported by Visual Analytics tools, which give the user a deeper insight into the correspondence between data and model result. Combining both approaches, better models for segmentation of organs in medical images are created. In this work, we identify the main tasks and problems in model-based image segmentation. As a proof of concept, we show that already small visual-interactive extensions can be very beneficial. Based on these results, we present research challenges for Visual Analytics in this area.