Detail publikace
Registration of medical image sequences using auto-differentiation
VIČAR, T. JAKUBÍČEK, R. CHMELÍK, J. KOLÁŘ, R.
Originální název
Registration of medical image sequences using auto-differentiation
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper focuses on image registration using the automatic differentiation of deep learning frameworks. Specifically, a method for the registration of image sequences is proposed and tested on retinal video ophthalmoscopic data and brain DCE MR images. PyTorch auto-differentiation has been used as a core of an optimisation tool to find the optimal image transformation parameters. It allows us to easily design a loss function for our registration tasks. The image registration was achieved by simultaneous registration of all images using a global loss function without the need of the reference frame.
Klíčová slova
medical image registration; auto-differentiation; deep learning frameworks; gradient-based optimisation; video stabilisation
Autoři
VIČAR, T.; JAKUBÍČEK, R.; CHMELÍK, J.; KOLÁŘ, R.
Vydáno
21. 12. 2023
Nakladatel
Springer
ISBN
978-981-16-6774-9
Kniha
Medical Imaging and Computer-Aided Diagnosis: Proceedings of 2022 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2022)
Edice
1
ISSN
1876-1100
Periodikum
Lecture notes in Electrical Engineering
Ročník
810
Stát
Spojené království Velké Británie a Severního Irska
Strany od
169
Strany do
178
Strany počet
10
URL
BibTex
@inproceedings{BUT180041,
author="Tomáš {Vičar} and Roman {Jakubíček} and Jiří {Chmelík} and Radim {Kolář}",
title="Registration of medical image sequences using auto-differentiation",
booktitle="Medical Imaging and Computer-Aided Diagnosis: Proceedings of 2022 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2022)",
year="2023",
series="1",
journal="Lecture notes in Electrical Engineering",
volume="810",
pages="169--178",
publisher="Springer",
doi="10.1007/978-981-16-6775-6\{_}15",
isbn="978-981-16-6774-9",
issn="1876-1100",
url="https://link.springer.com/chapter/10.1007/978-981-16-6775-6_15"
}