Publication detail

Supervised Segmentation for 3D Slicer

CHALUPA, D.

Original Title

Supervised Segmentation for 3D Slicer

Type

conference paper

Language

English

Original Abstract

The purpose of this work is to introduce an extendable framework for training and usage of machine learning algorithms. This framework is bundled in an extension for 3D Slicer that is to be used for medical images segmentation. An example usage of the extension is also provided.

Keywords

3D Slicer, C++, extension, machine learning, optimization, segmentation, tomography

Authors

CHALUPA, D.

Released

27. 4. 2017

ISBN

978-80-214-5496-5

Book

Proceedings of the 23rd Conference STUDENT EEICT 2017

Pages from

296

Pages to

298

Pages count

3

BibTex

@inproceedings{BUT139550,
  author="Daniel {Chalupa}",
  title="Supervised Segmentation for 3D Slicer",
  booktitle="Proceedings of the 23rd Conference STUDENT EEICT 2017",
  year="2017",
  pages="296--298",
  isbn="978-80-214-5496-5"
}