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Volume: 29 | Article ID: art00003
A Model Based Neuron Detection Approach using Sparse Location Priors
  DOI :  10.2352/ISSN.2470-1173.2017.17.COIMG-417  Published OnlineJanuary 2017

In order to accurately monitor neural activity in a living mouse brain, it is necessary to image each neuron at a high frame rate. Newly developed genetically encoded calcium indicators like GCaMP6 have fast kinetic response and can be used to target specific cell types for long duration. This enables neural activity imaging of neuron cells with high frame rate via fluorescence microscopy. In fluorescence microscopy, a laser scans the whole volume and the imaging time is proportional to the volume of the brain scanned. Scanning the whole brain volume is time consuming and fails to fully exploit the fast kinetic response of new calcium indicators. One way to increase the frame rate is to image only the sparse set of voxels containing the neurons. However, in order to do this, it is necessary to accurately detect and localize the position of each neuron during the data acquisition. In this paper, we present a novel model-based neuron detection algorithm using sparse location priors. We formulate the neuron detection problem as an image reconstruction problem where we reconstruct an image that encodes the location of the neuron centers. We use a sparsity based prior model since the neuron centers are sparsely distributed in the 3D volume. The information about the shape of neurons is encoded in the forward model using the impulse response of a filter and is estimated from training data. Our method is robust to illumination variance and noise in the image. Furthermore, the cost function to minimize in our formulation is convex and hence is not dependent on good initialization. We test our method on GCaMP6 fluorescence neuron images and observe better performance than widely used methods.

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Soumendu Majee, Dong Hye Ye, Gregery T. Buzzard, Charles A. Bouman, "A Model Based Neuron Detection Approach using Sparse Location Priorsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XV,  2017,  pp 10 - 17,

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