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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TE01020415" target="_blank" >TE01020415: Centrum kompetence ve zpracování vizuálních informací (V3C - Visual Computing Competence Center)</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-989-758-278-3

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

124-131

Název nakladatele

Institute for Systems and Technologies of Information, Control and Communication

Místo vydání

Funchal

Místo konání akce

Funchal, Madeira - Portugal

Datum konání akce

19. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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