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Biomedical Applications: From Data Capture to Modeling

Cindy Grimm, Oregon State University
G. Elisabeta Marai, University of Pittsburgh

Pages: pp. 20-21

The incredible array of image and data-capture tools available to the scientific community is dramatically changing our understanding of biological processes. For example, imaging modalities such as computed tomography and magnetic resonance imaging let us visualize and track complex biological processes such as beating hearts.

Data capture is only part of the story, however. Just as calculus helps physicists understand and model the mechanical world, computational models can help us model complex biological systems and reason and make predictions about them. Computer graphics and visualization techniques and algorithms—from modeling to physically based animation—make this process possible. This special issue is dedicated to the multidisciplinary effort needed to build, verify, and understand biological models.

We start with “Mesh Processing in Medical-Image Analysis—a Tutorial.” Rather than completely covering state-of-the-art techniques, Joshua Levine and his colleagues focus on introductory material for new audiences: modern scanning modalities, constructing geometric models, building 2D and 3D meshes to represent these domains, and important biological applications that use this image-to-mesh pipeline. In a timely illustration of this pipeline, Nahyup Kang and his colleagues pre-sent “Simulating Liver Deformation during Respiration Using Sparse Local Features.”

Terry Yoo and his colleagues go a step further in “Visualizing Cells and Humans in 3D: Biomedical Image Analysis at Nanometer and Meter Scales.” Despite the difference in subject and scale, they find that the methods used for analysis and modeling are often remarkably similar. These methods derive from image processing, computer vision, and computer graphics techniques, including medical illustration, visualization, and rapid prototyping.

From the applications end, in “uPy: A Ubiquitous CG Python API with Biological-Modeling Applications,” Ludovic Autin and his colleagues describe a publicly available Python extension module to simplify programming for science applications across graphics APIs such as Blender, Maya, Cinema4D, and DejaVu. In “Molli: Interactive Visualization for Exploratory Protein Analysis,” Sara Su and her colleagues describe a prototype system for interactive visualization and analysis of protein structures. Finally, in “A Practical Workflow for Making Anatomical Atlases for Biological Research,” Yong Wan and his colleagues use artistic tools to generate 3D anatomical atlases. In particular, they developed a mouse limb atlas to study the development of the mouse musculoskeletal system.

The range of topics covered in this issue highlights the wide range of challenges in applying existing computer graphics techniques to model biological data. With this analysis and formalization of our collective experiences, we hope to motivate computer graphics researchers to think about new problems and new approaches to persistent problems in biomedical modeling.

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