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Dusica Maric

Researcher at University of Novi Sad

Publications -  30
Citations -  232

Dusica Maric is an academic researcher from University of Novi Sad. The author has contributed to research in topics: Dentate nucleus & Cerebellar cortex. The author has an hindex of 8, co-authored 30 publications receiving 208 citations.

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Morphology and classification of large neurons in the adult human dentate nucleus: A qualitative and quantitative analysis of 2D images

TL;DR: This study qualitatively analyze a sample of large neurons according to their morphology and topology, and classify these cells into four types, taking into account seven morphometric parameters which describe the main properties of the cell soma, dendritic field and dendrite arborization.
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Application of fractal analysis to neuronal dendritic arborisation patterns of the monkey dentate nucleus.

TL;DR: It is proved that by application of fractal analysis to the dendritic arbours of these cells whilst ignoring other neuronal attributes allows for clear discrimination of only three cell types.
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Morphology and cell classification of large neurons in the adult human dentate nucleus: a quantitative study.

TL;DR: This study quantified the morphology of the large dentate neurons in the adult human taking, into account seven morphometric parameters that describe the main properties of the cell soma, thedendritic field and the dendritic branching pattern, and proved that the sample of human dentate neuron can be classified into four distinct types.
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An anatomical study of the lumbar external foraminal ligaments: appearance at MR imaging.

TL;DR: The MRI appearance of the ligaments within the external space of the lumbar intervertebral foramen is examined by correlating MR images with the corresponding anatomic dissection of the cadaverLumbar spine to confirm that external transforaminal ligaments are common structures in the interverTEbral foramina.
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Mathematical modeling of the neuron morphology using two dimensional images.

TL;DR: The low value of mean relative percent deviation (MRPD) between the experimental data and the predicted neuron size obtained by RSM model showed that model was suitable for modeling the size of DN neurons, and RSM can be generally used for modeling neuron size from 2D images.