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Detail publikace
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
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
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.
Klíčová slova
heart axis determination, regression, deep-learning, MRI
Autoři
JURČA, J.; HARABIŠ, V.; JAKUBÍČEK, R.; HOLEČEK, T.; NEMČEKOVÁ, P.; OUŘEDNÍČEK, P.; CHMELÍK, J.
Vydáno
4. 1. 2024
Nakladatel
Springer
Místo
Cham
ISBN
978-3-031-49061-3
Kniha
MEDICON’23 and CMBEBIH’23
Edice
93
Číslo edice
1
ISSN
1680-0737
Periodikum
IFMBE PROCEEDINGS
Ročník
Stát
Švédské království
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" }