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Automatically tracking neurons in a moving and deforming brain

Fig 4

Schematic of Neuron Registration Vector Encoding.

(A) The registration between a sample volume and a single reference volume is done in several steps. I. The image is segmented into regions corresponding to each of the neurons. II. The image is represented as a Gaussian mixture, with a single Gaussian for each segmented region. The amplitude and the standard deviation of the Gaussians are derived from the brightness and the size of the segmented regions. III. Non-rigid point-set registration is then used to deform the sample points to best overlap the reference point-set. IV. Neurons from the sample and the reference point-sets are paired by minimizing distances between neurons. (B) Neuron registration vectors are constructed by assigning a feature vector vi,t to each neuron xi,t in a sample volume xt by performing the registration between the sample volume and a set of 300 reference volumes, each denoted by rk. Each registration of the neuron results in a neuron match, , and the set of matches becomes the feature vector vi,t. (C) Vectors from all neuron-times are clustered into similar groups in a two step process: Hierarchical clustering (illustrated in the figure) is performed on a subset of neurons to define clusters, each of which is given a label Sn. Then each feature vector vi,t is assigned to a cluster based on a distance metric (not illustrated). (D) The clustering of the feature vectors shown in (C) assigns an identity to each of the neurons in every volume. This allows us to track the neurons across different volumes of the recording.

Fig 4

doi: https://doi.org/10.1371/journal.pcbi.1005517.g004