A general problem in neuroanatomy arises from the difficulty to acquire sufficient positional neuronal data to accurately quantify neuronal disruptions in the brain. This problem is particularly serious for assessing disruptions caused by neuropathological diseases, such as Alzheimer's. The task of manually collecting neuronal positions is extremely time-consuming. Moreover, the number of neuronal positions needed in, e.g., a study of microcolumnar structures in the human cortical lining of the superior temporal sulcus is immense, ranging up to tens of thousands of neurons.

Peng et al. present a systematic study that addresses this need. The authors developed a fully automated method, which takes as input digitized pictures of tissue and produces numerical output corresponding to the spatial coordinates of the identified neurons in the picture. The method, called a parallel Potts segmentation method, is based on concepts previously developed in condensed matter physics for understanding magnetic materials. In this method, a computer replaces each pixel by a "virtual magnet"...  Read more

A general problem in neuroanatomy arises from the difficulty to acquire sufficient positional neuronal data to accurately quantify neuronal disruptions in the brain. This problem is particularly serious for assessing disruptions caused by neuropathological diseases, such as Alzheimer's. The task of manually collecting neuronal positions is extremely time-consuming. Moreover, the number of neuronal positions needed in, e.g., a study of microcolumnar structures in the human cortical lining of the superior temporal sulcus is immense, ranging up to tens of thousands of neurons.

Peng et al. present a systematic study that addresses this need. The authors developed a fully automated method, which takes as input digitized pictures of tissue and produces numerical output corresponding to the spatial coordinates of the identified neurons in the picture. The method, called a parallel Potts segmentation method, is based on concepts previously developed in condensed matter physics for understanding magnetic materials. In this method, a computer replaces each pixel by a "virtual magnet" that points in a direction assigned by the relative darkness of the pixel. Then, the method groups together magnets pointing in the same direction and produces a patchwork of "islands" that directly correspond to different islands in the original picture. From each of the thousands of resulting islands, the method then decides whether the island corresponds to a neuron or not, by selecting islands based on size, shape and relative darkness.

To validate their new method, Peng et al. apply it to digitized pictures of tissue from healthy humans and patients with AD. They find that their method can identify up to 98 percent of all neurons in healthy subjects and up to 93 percent of all neurons in AD cases. The number of false positives do not account for more than 3 percent of all identified neurons.

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