@Article{CiCP-28-3,
author = {Pawar, Aishwarya and Zhang, Yongjie, Jessica},
title = {Computational Software: NeuronSeg_BACH: Automated Neuron Segmentation Using B-Spline Based Active Contour and Hyperelastic Regularization},
journal = {Communications in Computational Physics},
year = {2020},
volume = {28},
number = {3},
pages = {1219--1244},
abstract = {<p style="text-align: justify;">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 the
thin branch-like fibrous structure of neurons make the process of manual segmentation
challenging. We propose a novel automatic neuron segmentation framework using a
B-spline based active contour deformation model with hyperelastic regularization, and
develop a MATLAB software tool named &quot;NeuronSeg<span style="font-family: 宋体;">_</span>BACH&quot;. In NeuronSeg<span style="font-family: 宋体;">_</span>BACH,
initialization of the contour is done automatically by detecting cell body and neurites
separately. This boundary-extraction based algorithm utilizes cubic B-splines to deform active contours to match the neuron cell surface accurately. Using adaptive local
refinement, finer level deformation of the active contour is captured using truncated
hierarchical B-splines in a multiresolution manner. By introducing hyperelastic regularization, we allow large nonlinear deformations of the active contours. Unlike other
existing methods which represent boundaries as piecewise constant functions, we provide a more accurate and smooth representation of the neuron geometry. In the level
set segmentation framework, the implicit level set function is defined using $C^2$<sup>&nbsp;</sup>continuous B-splines. Improved segmentation results are shown for 2D and 3D synthetic
and microscopy images as compared to other methods.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2020-0025},
url = {https://global-sci.com/article/79719/computational-software-neuronseg-bach-automated-neuron-segmentation-using-b-spline-based-active-contour-and-hyperelastic-regularization}
}