Detail publikačního výsledku

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

CHMELÍK, J.; JAKUBÍČEK, R.; VIČAR, T.; WALEK, P.; OUŘEDNÍČEK, P.; JAN, J.

Originální název

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

Anglický název

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

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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).

Anglický abstrakt

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).

Klíčová slova

CT; brain; alignement; machine learning

Klíčová slova v angličtině

CT; brain; alignement; machine learning

Autoři

CHMELÍK, J.; JAKUBÍČEK, R.; VIČAR, T.; WALEK, P.; OUŘEDNÍČEK, P.; JAN, J.

Rok RIV

2020

Vydáno

07.10.2019

Nakladatel

IEEE

Místo

Berlin, Germany

ISBN

978-1-5386-1312-2

Kniha

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

ISSN

1557-170X

Periodikum

Proceedings IEEE EMBC

Svazek

19

Stát

Spojené státy americké

Strany od

4404

Strany do

4408

Strany počet

5

URL

https://ieeexplore.ieee.org/document/8857858

BibTex

@inproceedings{BUT157792,
  author="Jiří {Chmelík} and Roman {Jakubíček} and Tomáš {Vičar} and Petr {Walek} and Petr {Ouředníček} and Jiří {Jan}",
  title="Iterative machine learning based rotational alignment of brain 3D CT data",
  booktitle="2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
  year="2019",
  journal="Proceedings IEEE EMBC",
  volume="19",
  number="19",
  pages="4404--4408",
  publisher="IEEE",
  address="Berlin, Germany",
  doi="10.1109/EMBC.2019.8857858",
  isbn="978-1-5386-1312-2",
  issn="1557-170X",
  url="https://ieeexplore.ieee.org/document/8857858"
}