Iterative machine learning based rotational alignment of brain 3D CT data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU132791" target="_blank" >RIV/00216305:26220/19:PU132791 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8857858" target="_blank" >https://ieeexplore.ieee.org/document/8857858</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMBC.2019.8857858" target="_blank" >10.1109/EMBC.2019.8857858</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Iterative machine learning based rotational alignment of brain 3D CT data

Popis výsledku v původním jazyce

The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (1 degree) and in a significantly shorter time than the experts (2 minutes per case).

Název v anglickém jazyce

Iterative machine learning based rotational alignment of brain 3D CT data

Popis výsledku anglicky

The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (1 degree) and in a significantly shorter time than the experts (2 minutes per case).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

ISBN

978-1-5386-1312-2

ISSN

1557-170X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

4404-4408

Název nakladatele

IEEE

Místo vydání

Berlin, Germany

Místo konání akce

Berlin

Datum konání akce

23. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000557295304193