Statistical shape models (SSMs) have been firmly established as a robust tool for medical image
analysis but the application of this technology in the MedTech industry is still in its early
stage. This can be explained by the fact that the construction and application of statistical
shape models is very knowledge-intensive and requires a large number of high quality training
examples, which are not readily-available to the MedTech industry. In this project, we will leve-
rage the technical advancements on statistical shape modeling achieved within the Co-Me re-
search network. Integrating the various research results on image segmentation and registra-
tion, statistical shape model fitting, and medical image collection, will provide a dedicated user
platform as part of a Knowledge and Technology Transfer (KTT) framework. In particular, this
platform will integrate the virtual skeleton database (VSD) from the same titled Co-Me phase 3
project. Furthermore, it will feature an SSM toolkit, which will allow for the straightforward
application of such models.
As a proof of concept and as an exemplar application of the proposed platform, we will further
develop a medical application called iLeg for the advanced treatment of knee osteoarthritis (OA)
disease. With a unique SSM-based 2D/3D reconstruction technique, iLeg targets for true 3D
planning and evaluation of OA treatment from clinically available 2D X-ray radiographs, whose
acquisition is part of the standard treatment loop. Hospital integration of this application
through collaborative research with our clinical partners and the integration into the product
portfolios of our industrial partners will demonstrate the efficacy of the proposed platform.
The benefits of the proposed platform are multifold. First, it will serve as an engine for Co-Me
and related research groups to generate and promote funding through projects ranging from
basic research to industrial collaboration. The goal is that the further development and appli-
cation of this platform should be self-sustainable even beyond the Co-Me lifespan. Second, it
will bring added value to the Swiss MedTech industry by boosting the development of new di-
agnostic and interventional technologies, the design of new implants, and the optimization of
the product supply chain management for cost reduction, positioning them as innovation lead-
ers. Last but not least, it will bring true benefits to patients enabling efficient solutions for im-
proved accuracy and safety of disease treatment.
Statistical shape models (SSMs) have been firmly established as a robust tool for medical image analysis but the application of this technology in the MedTech industry is still in its early stage. This can be explained by the fact that the construction and application of statistical shape models is very knowledge-intensive and requires a large number of high quality training examples, which are not readily-available to the MedTech industry. In this project, we will leverage the technical advancements on statistical shape modeling achieved within the Co-Me re-search network. Integrating the various research results on image segmentation and registration, statistical shape model fitting, and medical image collection, will provide a dedicated user platform. The proposed platform will be based on the Virtual Skeleton Database, a large repository of medical images. Furthermore, it will feature an Shape modeling toolkit, which will allow for the straightforward application of such models. As an exemplar application of the proposed platform, we will develop a medical application called iLeg for the advanced treatment of knee osteoarthritis (OA)disease. iLeg targets true 3D planning and evaluation of OA treatment from clinically available 2D X-ray radiographs, whose acquisition is part of the standard treatment loop.
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