A Novel Tool for Supervised Segmentation Using 3D Slicer

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU129719" target="_blank" >RIV/00216305:26220/18:PU129719 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2073-8994/10/11/627" target="_blank" >https://www.mdpi.com/2073-8994/10/11/627</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/sym10110627" target="_blank" >10.3390/sym10110627</a>

Result language
angličtina
Original language name
A Novel Tool for Supervised Segmentation Using 3D Slicer
Original language description
The rather impressive extension library of medical image-processing platform 3D Slicer lacks a wide range of machine-learning toolboxes. The authors have developed such a toolbox that incorporates commonly used machine-learning libraries. The extension uses a simple graphical user interface that allows the user to preprocess data, train a classifier, and use that classifier in common medical image-classification tasks, such as tumor staging or various anatomical segmentations without a deeper knowledge of the inner workings of the classifiers. A series of experiments were carried out to showcase the capabilities of the extension and quantify the symmetry between the physical characteristics of pathological tissues and the parameters of a classifying model. These experiments also include an analysis of the impact of training vector size and feature selection on the sensitivity and specificity of all included classifiers. The results indicate that training vector size can be minimized for all classifiers. Using the data from the Brain Tumor Segmentation Challenge, Random Forest appears to have the widest range of parameters that produce sufficiently accurate segmentations, while optimal Support Vector Machines’ training parameters are concentrated in a narrow feature space.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20601 - Medical engineering

Project
<a href="/en/project/NV18-08-00459" target="_blank" >NV18-08-00459: Spatial Analysis of the Force Load on a Deformed Developing Spine, and Corrective Force Modelling Applied to Minimize the Scope of a Scoliosis Surgery.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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