Visual Analytics Methods for Modeling in Medical Imaging

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Medical imaging plays an important role in clinical practice, for example in treatment planning or computer-aided diagnosis. In this respect, segmentation of medical images is a necessary prerequisite. Frequently used segmentation algorithms are based on statistical shape models (SSMs). By modeling an organ’s shape variability, they enable segmentation of organs which can not be segmented using image intensities only. For building an SSM, models have to be selected that fit the high-dimensional training data well. Due to the lack of prior information on the data, standard models are frequently chosen. However, they do not necessarily describe the data in an optimal way. A poor choice of the model is not apparent until the segmentation algorithm is evaluated. Visual analytics methods can provide valuable tools for supporting this modeling process.The aim of this project is to develop new Visual Analytics methods for fitting SSMs in medical image segmentation. Our approach combines interactive data visualization, data analysis and model steering in all stages of the process. We follow a “closed-loop” concept with feedback loops allowing for refining models interactively. In this way, the user is provided with a deeper insight into the correspondence between data and model result. As an outcome, better models for segmentation of organs in medical images will be created.