Detail publikačního výsledku

Deep-learning based automatic determination of cardiac planes in survey MRI data

JURČA, J.; HARABIŠ, V.; JAKUBÍČEK, R.; HOLEČEK, T.; NEMČEKOVÁ, P.; OUŘEDNÍČEK, P.; CHMELÍK, J.

Originální název

Deep-learning based automatic determination of cardiac planes in survey MRI data

Anglický název

Deep-learning based automatic determination of cardiac planes in survey MRI data

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Inference of the radiological planes of the heart in MRI is a crucial step for valid data acquisition to examine the structure and function of the human heart in detail. In this paper, we present a deep learning model for automatic inference of the radiological plane of the heart from 3D survey sequences. The proposed neural network is based on the V-Net~\cite{vnet} architecture that has been developed to perform inference on the radiological positions of the hearts. The network is designed to take a 3D image as input and generate a regressed heatmap of probable plane positions as output. The results show that the proposed method is feasible for automatic geometry planning. It has the potential to increase the efficiency of medical imaging. The presented networks show that they can locate cardiac landmarks even from data with anisotropic voxels. It can improve the accuracy and speed of diagnosis, allowing for faster and more effective treatment.

Anglický abstrakt

Inference of the radiological planes of the heart in MRI is a crucial step for valid data acquisition to examine the structure and function of the human heart in detail. In this paper, we present a deep learning model for automatic inference of the radiological plane of the heart from 3D survey sequences. The proposed neural network is based on the V-Net~\cite{vnet} architecture that has been developed to perform inference on the radiological positions of the hearts. The network is designed to take a 3D image as input and generate a regressed heatmap of probable plane positions as output. The results show that the proposed method is feasible for automatic geometry planning. It has the potential to increase the efficiency of medical imaging. The presented networks show that they can locate cardiac landmarks even from data with anisotropic voxels. It can improve the accuracy and speed of diagnosis, allowing for faster and more effective treatment.

Klíčová slova

heart axis determination, regression, deep-learning, MRI

Klíčová slova v angličtině

heart axis determination, regression, deep-learning, MRI

Autoři

JURČA, J.; HARABIŠ, V.; JAKUBÍČEK, R.; HOLEČEK, T.; NEMČEKOVÁ, P.; OUŘEDNÍČEK, P.; CHMELÍK, J.

Rok RIV

2025

Vydáno

04.01.2024

Nakladatel

Springer

Místo

Cham

ISBN

978-3-031-49061-3

Kniha

MEDICON’23 and CMBEBIH’23

Edice

93

ISSN

1680-0737

Periodikum

IFMBE Proceedings

Svazek

93

Stát

Francouzská republika

Strany od

285

Strany do

292

Strany počet

8

URL

https://link.springer.com/chapter/10.1007/978-3-031-49062-0_31

BibTex

@inproceedings{BUT185645,
  author="Jan {Jurča} and Vratislav {Harabiš} and Roman {Jakubíček} and Tomáš {Holeček} and Petra {Nemčeková} and Petr {Ouředníček} and Jiří {Chmelík}",
  title="Deep-learning based automatic determination of cardiac planes in survey MRI data",
  booktitle="MEDICON’23 and CMBEBIH’23",
  year="2024",
  series="93",
  journal="IFMBE Proceedings",
  volume="93",
  number="1",
  pages="285--292",
  publisher="Springer",
  address="Cham",
  doi="10.1007/978-3-031-49062-0\{_}31",
  isbn="978-3-031-49061-3",
  issn="1680-0737",
  url="https://link.springer.com/chapter/10.1007/978-3-031-49062-0_31"
}