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Found 4 papers, 1 papers with code
Preserving Derivative Information while Transforming Neuronal Curves
Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between.
Hidden Markov Modeling for Maximum Likelihood Neuron Reconstruction
Our most probable estimation method models the task of reconstructing neuronal processes in the presence of other neurons, and thus is applicable in images with several neurons.
Fitting Splines to Axonal Arbors Quantifies Relationship between Branch Order and Geometry
Our representation makes it possible to compute these parameters from neuron traces in closed form.
AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python
Background: Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation.
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