3D Image Segmentation using Graph-Cut and Random Forests Learned from Partial Annotations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU126812" target="_blank" >RIV/00216305:26230/18:PU126812 - isvavai.cz</a>
Result on the web
<a href="http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=3oP1dAKzK9U=&t=1" target="_blank" >http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=3oP1dAKzK9U=&t=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0006588801240131" target="_blank" >10.5220/0006588801240131</a>
Alternative languages
Result language
angličtina
Original language name
3D Image Segmentation using Graph-Cut and Random Forests Learned from Partial Annotations
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TE01020415" target="_blank" >TE01020415: V3C - Visual Computing Competence Center</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING
ISBN
978-989-758-278-3
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
124-131
Publisher name
Institute for Systems and Technologies of Information, Control and Communication
Place of publication
Funchal
Event location
Funchal, Madeira - Portugal
Event date
Jan 19, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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