We developed NeuronGrowth, a software for the automatic quantification of extension and retraction of neurites and filopodia, from time-lapse sequences of two-dimensional digital micrographs. NeuronGrowth requires a semiautomatic characterization of individual neurites in a reference frame, which is then used for automatic tracking and measurement of every neurite over the whole image sequence. Modules for sequence alignment, background subtraction, flat field correction, light normalization, and cropping have been integrated to improve the quality of the analysis. Moreover, NeuronGrowth incorporates a deconvolution filter that corrects the shadow-cast effect of differential interference contrast (DIC) images. NeuronGrowth was tested by analyzing the formation of outgrowth patterns by individual leech neurons cultured under two different conditions. Phase contrast images were obtained from neurons plated on CNS homogenates and DIC images were obtained from similar neurons plated on ganglion capsules as substrates. Filopodia were measured from fluorescent growth-cones of chick dorsal root ganglion cells. Quantitative data of neurite extension and retraction obtained by three different users applying NeuronGrowth and two other manually operated software packages were similar. However, NeuronGrowth required less user participation and had a better time performance when compared with the other software packages. NeuronGrowth may be used in general to quantify the dynamics of tubular structures such as blood vessels. NeuronGrowth is a free plug-in for the free software ImageJ and can be downloaded along with a user manual, a troubleshooting section and other information required for its use from http://www.ifc.unam.mx or http://www.ifc.unam.mx/ffm/index.html.
We present a method for semi-automatic tracing and measuring of neurite outgrowth from time-lapse sequences of digital Nomarski micrographs. The algorithm is based on neurite ridge extraction and characterization from a single frame, followed by an automatic neurite tracking and measurement along the image sequence. Our method was tested with two sequences one containing 29 and other with 77 frames taken at intervals of 2 min. Our method rendered comparable length measurements but better time performance than measurements made by use of certain public software.
X-ray imaging is currently the gold standard for the assessment of spinal deformities. The purpose of this study is to evaluate a freehand 3D ultrasound system for volumetric reconstruction of the spine. A setup consisting of an ultrasound scanner with a linear transducer, an electromagnetic measuring system and a workstation was used. We conducted 64 acquisitions of US images of 8 adults in a natural standing position, and we tested three setups: 1) Subjects are constrained to be close to a wall, 2) Subjects are unconstrained, and 3) Subjects are constrained to performing fast and slow acquisitions. The spinous processes were manually selected from the volume reconstruction from tracked ultrasound images to generate a 3D point-based model depicting the centerline of the spine. The results suggested that a freehand 3D ultrasound system can be suitable for representing the spine. Volumetric reconstructions can be computed and landmarking can be performed to model the surface of the spine in the 3D space. These reconstructions promise to generate computer-based descriptors to analyze the shape of the spine in the 3D space.Clinical Relevance-We provide clinicians with a protocol that could be integrated in clinical setups for the assessment and monitoring of AIS, based on US image acquisitions, which constitutes a radiation-free technology.
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