Virtual Brain - An anatomy learning software for medical students
Virtual Brain is a joint project
with the HKU Department of Pathology. The purpose of this investigation is to enhance the learning of the neuroanatomy and skeletal anatomy of the normal brain
using a computer based approach. Traditional neuroanatomy has been taught using sections of the brain. However, with the shift in emphasis towards a medical
curriculum which stresses more importance on learning through the use of computers, a supporting software in addition to the regular teaching which takes part
is much desired.
Due to the huge volume of human body data set, most current supporting systems either use static 2D illustrations or rely on expensive high-end graphic
workstations for interactive 3D visualization. We therefore aim at developing a software package that would enable students to “walk through” the brain and
visualize the structures in their correct anatomical and spatial context on PC platform. Teachers and students can interact with the virtual brain and study the
Using volume data from the Visible Human Project (VHP), a 3D surface model of the normal human brain, skull and various anatomical structures can be
constructed. The Virtual Brain software can also import existing 3D models so that the data sets are not only limited to that of the VHP, but also enriched with
auxiliary structures such as facial muscles and skin. The software also supports quiz and answer module for self-assessment purposes.
The human brain model alone consists mainly of ten components, which have in total 204,414 polygons. With some dedicated computer graphics speedup
techniques, not only the human brain can be visualized, but the entire human body can be incorporated into the system for a more comprehensive study. The major
challenge is to make interactive visualization possible on desktop PC so that the entire software system can be made readily available to medical professionals
For improving the frame rate in our system, we have experimented several techniques, such as level of details, occlusion culling and transparency ordering. With
the aid of several speedup techniques, the interactive frame rate (>20fps) of rendering the whole human brain data has been attained.