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CHALUPA, D. MIKULKA, J.
Originální název
A Novel Tool for Supervised Segmentation Using 3D Slicer
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
3D slicer; classification; extension; random forest; segmentation; sensitivity analysis; support vector machine; tumor
Autoři
CHALUPA, D.; MIKULKA, J.
Vydáno
12. 11. 2018
Nakladatel
MDPI
ISSN
2073-8994
Periodikum
Symmetry
Ročník
10
Číslo
11
Stát
Švýcarská konfederace
Strany od
1
Strany do
9
Strany počet
URL
https://www.mdpi.com/2073-8994/10/11/627
Plný text v Digitální knihovně
http://hdl.handle.net/11012/137219
BibTex
@article{BUT151184, author="Daniel {Chalupa} and Jan {Mikulka}", title="A Novel Tool for Supervised Segmentation Using 3D Slicer", journal="Symmetry", year="2018", volume="10", number="11", pages="1--9", doi="10.3390/sym10110627", issn="2073-8994", url="https://www.mdpi.com/2073-8994/10/11/627" }