Segmentation of 2 1/2 D brain image stacks with automatic extraction and visualization of functional information

The authors present a new method for the segmentation of autoradiographic brain image stacks, as well as the automatic extraction and 3D visualization of functional information encoded in the image gray values. The difficulty in the evaluation of these so-called "2 1/2 D" data sets is that they do not inherently represent a continuous 3D data volume (as MRI or CT), but consist of a series of consecutive, strongly distorted gray value images of single tissue slices, while the exact position and direction of the original slices varies among different data sets. This complicates the comparison of different experiments. In this work, in addition to providing an enhanced active contour based segmentation algorithm, the concept of "map space" is introduced, which allows the automatic functional evaluation using a shape adaptive, rectangular, normalized, parametric representation of the cortex. Results are presented for a rat brain under acoustical stimulation.