Workshop on Statistical and Shape-based Image Analysis With Applications in Medicine

Statistical shape models are one of the most important technologies in medical image analysis. With this technology, the computer learns the characteristic shape variations of an organ. The model resulting from this analysis may then be used in implant design, image analysis, surgery planning and many other fields. In this course, we will give an introduction to statistical shape models, from the unifying perspective of Gaussian processes. Besides presenting an overview of the main theoretical ideas underlying these models, we will show how these methods can be applied in practice. In tutorial sessions, the course participants will build their own shape model and apply it for the reconstruction of partial surfaces, using the open source software Scalismo.

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Recent medical image registration methods based on deep neural networks have seemly converged to so-called end-to-end learning approaches, in which the networks are trained to directly predict spatial transformation between a given pair of unprocessed images. Besides fast inference that enables sub-second volumetric registration, some have shown superior accuracy over the classical methods. I will first give a technical overview of the classical medical image registration and the state-of-the-art deep-learning-based examples, including the unsupervised and the (weakly-) supervised methods. I will conclude this talk with an introduction to our latest development using a more data-driven attempt to replace the transformation-predicting alternatives.

In this talk I will present computational and analytical tools we have been developing at University of Utah for the analysis of anatomical image ensembles that are designed to capture changes in anatomy. The fundamental analytical framework we have been using is that of regression analysis where the dependent variable is the anatomical configuration while the independent variable is application domain specific. I will exemplify the application of this general methodology to various medical imaging applications ranging from the analysis of Internal Organ Motion as imaged in 4D respiratory correlated CT imaging spanning few minutes to that of the study changes in brain anatomy associated with normal aging and neurodegenerative diseases such as Alzheimer’s spanning decades.

Manifold parameterizations have been applied to various fields of commercial industries. Several efficient algorithms for the computation of triangular surface mesh parameterizations have been proposed in recent years. However, the computation of tetrahedral volumetric mesh parameterizations is more challenging due to the fact that the number of mesh points would become enormously large when the higher-resolution mesh is considered and the bijectivity of parameterizations is more difficult to guarantee. In this talk, we develop a novel volumetric stretch energy minimization algorithm for volume-preserving parameterizations of simply connected 3-manifolds with a single boundary under the restriction that the boundary is a spherical area-preserving mapping. In addition, our algorithm can also be applied to compute spherical angle- and area-preserving parameterizations of genus-zero closed surfaces, respectively. Several numerical experiments indicate that the developed algorithms are more efficient and reliable compared to other existing algorithms. Numerical results on applications of the manifold partition and the mesh processing for three-dimensional printing are demonstrated thereafter to show the robustness of the proposed algorithm.

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From price to reproducibility to ease of collaboration and dissemination, open source software is becoming increasingly important for scientific research. This is especially true in the biomedical domain. Having the newest and most advanced methodologies freely available can accelerate research efforts and positively impact millions of lives. In this talk I will discuss several projects where shape analysis was a key component to achieving deeper anatomical understanding and how this can be accomplished using open-source software.

Taiwan Society for Industrial and Applied Mathematics