Computational Software: NeuronSeg_BACH: Automated Neuron Segmentation Using B-Spline Based Active Contour and Hyperelastic Regularization (Communications in Computational Physics, 2020)
The vast diversity in neuron cell morphology has led to an increase in automated algorithms which can accurately reconstruct neurons from microscopy images. The poor quality of brightfield and fluorescence microscopy images and thethin branch-like fibrous structure of neurons make the process of manual segmentationchallenging. We propose a novel automatic neuron segmentation framework using aB-spline based active contour deformation model with hyperelastic regularization, anddevelop a MATLAB software tool named "NeuronSeg_BACH". In NeuronSeg_BACH,initialization of the contour is done automatically by detecting cell body and neuritesseparately. This boundary-extraction based algorithm utilizes cubic B-splines to deform active contours to match the neuron cell surface accurately. Using adaptive localrefinement, finer level deformation of the active contour is captured using truncatedhierarchical B-splines in a multiresolution manner. By introducing hyperelastic regularization, we allow large nonlinear deformations of the active contours. Unlike otherexisting methods which represent boundaries as piecewise constant functions, we provide a more accurate and smooth representation of the neuron geometry. In the levelset segmentation framework, the implicit level set function is defined using $C^2$ continuous B-splines. Improved segmentation results are shown for 2D and 3D syntheticand microscopy images as compared to other methods.