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Open AccessArticle

A Novel Tool for Supervised Segmentation Using 3D Slicer

Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 3082/12, 616 00 Brno, Czech Republic
Author to whom correspondence should be addressed.
Symmetry 2018, 10(11), 627;
Received: 15 October 2018 / Revised: 31 October 2018 / Accepted: 7 November 2018 / Published: 12 November 2018
(This article belongs to the Special Issue Symmetry in Engineering Sciences)
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. View Full-Text
Keywords: 3D slicer; classification; extension; random forest; segmentation; sensitivity analysis; support vector machine; tumor 3D slicer; classification; extension; random forest; segmentation; sensitivity analysis; support vector machine; tumor
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Chalupa, D.; Mikulka, J. A Novel Tool for Supervised Segmentation Using 3D Slicer. Symmetry 2018, 10, 627.

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