Abstract
The genesis of new cells, especially of neurons, in the adult human brain is currently of great scientific interest. In order to measure neurogenesis in animals new born cells are labelled with specific markers such as BrdU; in brain sections these can later be analyzed and counted through the microscope. So far, the image analysis has been performed by hand. In this work, we present an algorithm ...
Abstract
The genesis of new cells, especially of neurons, in the adult human brain is currently of great scientific interest. In order to measure neurogenesis in animals new born cells are labelled with specific markers such as BrdU; in brain sections these can later be analyzed and counted through the microscope. So far, the image analysis has been performed by hand. In this work, we present an algorithm to automatically segment the digital brain section picture into cell and noncell components, giving a count of the number of cells in the section. This is done by first training a so-called cell classifier with cell and non-cell patches in a supervised manner. This cell classifier can later be used in an arbitrary number of sections by scanning the section and choosing maxima of this classifier as cell center locations. For training, single- and multi-layer perceptrons were used. In preliminary experiments, we get good performance of the classifier.
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