Computer-aided diagnosis and therapy has evolved in response to the need for techniques that can assist clinicians identify conditions, plan treatments, and deliver therapy while reducing human error and achieving more accurate and precise disease diagnosis or treatment. However, despite the efforts of the academic research community to develop state-of-the-art algorithms and high performance techniques, their footprint often hampers their clinical use. Currently, the main challenge is not the lack of techniques for medical image analysis, and computing, but rather the lack of clinically feasible solutions that leverage the already developed techniques, as well as the clear demonstration of the potential clinical use and impact of these tools. This lecture will provide a few examples that demonstrate the implementation and evaluation of different image computing and navigation tools and illustrate their use across several clinical applications.

This presentation is part of Minisymposium “MS30 - Imaging, Modeling, Visualization and Biomedical Computing (2 parts)”
organized by: Cristian Linte (Biomedical Engineering and Center for Imaging Science, Rochester Institute of Technology) , Suzanne Shontz (University of Kansas) .

Authors:

Cristian Linte (Biomedical Engineering and Center for Imaging Science, Rochester Institute of Technology)

Keywords:

computer graphics, computer vision, image registration, image representation, image segmentation, inverse problems