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Detail publikačního výsledku
VIČAR, T.; JAKUBÍČEK, R.; CHMELÍK, J.; KOLÁŘ, R.
Originální název
Registration of medical image sequences using auto-differentiation
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
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.
Anglický abstrakt
Klíčová slova
medical image registration; auto-differentiation; deep learning frameworks; gradient-based optimisation; video stabilisation
Klíčová slova v angličtině
Autoři
Rok RIV
2025
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
Svazek
810
Stát
Nizozemsko
Strany od
169
Strany do
178
Strany počet
10
URL
https://link.springer.com/chapter/10.1007/978-981-16-6775-6_15
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" }