Virtual Finger Helps Study 3D Images Faster

When moving a cursor along the screen, the software recognizes whether the user is pointing to an object that is near, far, or somewhere in between, thus analyzing it without having to sift through many two-dimensional (2D) images to reach the correct level.

The VF software uses a family of “what you see is what you get” (WYSIWYG) computer algorithms that map inputs in the 2D plane of a computer screen to the 3D locations of biological entities in the volumetric space of a 3D image stack. The three types of objects produced with VF correspond to important structures found in typical fluorescent microscopic images: 3D points may mark locations of labelled cells or proteins, 3D curves may correspond to general vessel-like structures, and 3D regions of interest (ROI) may highlight specific cell populations or brain compartments.

Scientists at the Allen Institute are using the VF to improve detection of spikes from individual cells, and to better model the morphological structures of neurons. The technology is already being applied for instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and automated 3D reconstruction of neurons and similar biostructures, such as a projectome of a drosophila brain and studies the developing lung. The software and its applications were described in a study published on July 11, 2014, in Nature Communications.

“Using Virtual Finger could make data collection and analysis ten to 100 times faster, depending on the experiment,” said lead author Hanchuan Peng, PhD, associate investigator at the Allen Institute for Brain Science. “The software allows us to navigate large amounts of biological data in the same way that Google Earth allows you to navigate the world. It truly is a revolutionary technology for many different applications within biological science.”

Despite a number of advances on visualization of multidimensional image data and automated analysis of such data, a common bottleneck is the inability to efficiently explore the complicated 3D image content. This presents an obstacle for the unbiased, high-throughput and quantitative analysis of data and creates tremendous need for the development of new techniques that help explore 3D data directly and efficiently without expensive virtual reality devices.

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Allen Institute for Brain Science