Publication detail

3D Image Segmentation using Graph-Cut and Random Forests Learned from Partial Annotations

KODYM, O. ŠPANĚL, M.

Original Title

3D Image Segmentation using Graph-Cut and Random Forests Learned from Partial Annotations

Type

conference paper

Language

English

Original Abstract

Human tissue segmentation is a critical step not only in the process of their visualization and diagnostics but also for pre-operative planning and custom implants engineering. Manual segmentation of three-dimensional data obtained through CT scanning is very time demanding task for clinical experts and therefore the automation of this process is required. Results of fully automatic approaches often lack the required precision in cases of non-standard treatment, which is often the case when computer planning is important, and thus semi-automatic approaches demanding a certain level of expert interaction are being designed. This work presents a semi-automatic method of 3D segmentation applicable to arbitrary tissue that takes several manually annotated slices as an input. These slices are used for training a random forest classifiers to predict the annotation for the remaining part of the CT scan and final segmentation is obtained using the graph-cut method. Precision of the proposed method is evaluated  on CT datasets of hard tissue including tibia, humerus and radius bones, mandible and single teeth using the Dice coefficient of overlap compared to  fully expert-annotated segmentations of these tissues.

Keywords

Computed Tomography, Semi-automatic Segmentation, Random Forests, Graph-Cut

Authors

KODYM, O.; ŠPANĚL, M.

Released

21. 1. 2018

Publisher

Institute for Systems and Technologies of Information, Control and Communication

Location

Funchal

ISBN

978-989-758-278-3

Book

Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING

Pages from

124

Pages to

131

Pages count

7

URL

http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=3oP1dAKzK9U=&t=1

BibTex

@inproceedings{BUT145393,
  author="Oldřich {Kodym} and Michal {Španěl}",
  title="3D Image Segmentation using Graph-Cut and Random Forests Learned from Partial Annotations",
  booktitle="Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING",
  year="2018",
  pages="124--131",
  publisher="Institute for Systems and Technologies of Information, Control and Communication",
  address="Funchal",
  doi="10.5220/0006588801240131",
  isbn="978-989-758-278-3",
  url="http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=3oP1dAKzK9U=&t=1"
}