loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Oldřich Kodym 1 and Michal Španěl 2

Affiliations: 1 Brno University of Technology, Czech Republic ; 2 3Dim Laboratory, s.r.o., Czech Republic

Keyword(s): Computed Tomography, Semi-automatic Segmentation, Random Forests, Graph-cut.

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 various CT datasets using fully expert-annotated segmentations of these tissues. Dice coefficient of overlap is 0.976 ± 0.014 for hard tissue segmentation and 0.978 ± 0.008 for kidney segmentation, achieving competitive results with other task-specific methods. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.15.186.78

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kodym, O. and Španěl, M. (2018). Semi-automatic CT Image Segmentation using Random Forests Learned from Partial Annotations. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - BIOIMAGING; ISBN 978-989-758-278-3; ISSN 2184-4305, SciTePress, pages 124-131. DOI: 10.5220/0006588801240131

@conference{bioimaging18,
author={Old\v{r}ich Kodym. and Michal Španěl.},
title={Semi-automatic CT Image Segmentation using Random Forests Learned from Partial Annotations},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - BIOIMAGING},
year={2018},
pages={124-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006588801240131},
isbn={978-989-758-278-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - BIOIMAGING
TI - Semi-automatic CT Image Segmentation using Random Forests Learned from Partial Annotations
SN - 978-989-758-278-3
IS - 2184-4305
AU - Kodym, O.
AU - Španěl, M.
PY - 2018
SP - 124
EP - 131
DO - 10.5220/0006588801240131
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: [email protected]

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic