Publication detail

Registration of medical image sequences using auto-differentiation

VIČAR, T. JAKUBÍČEK, R. CHMELÍK, J. KOLÁŘ, R.

Original Title

Registration of medical image sequences using auto-differentiation

Type

conference paper

Language

English

Original Abstract

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.

Keywords

medical image registration; auto-differentiation; deep learning frameworks; gradient-based optimisation; video stabilisation

Authors

VIČAR, T.; JAKUBÍČEK, R.; CHMELÍK, J.; KOLÁŘ, R.

Released

21. 12. 2023

Publisher

Springer

ISBN

978-981-16-6774-9

Book

Medical Imaging and Computer-Aided Diagnosis: Proceedings of 2022 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2022)

Edition

1

ISBN

1876-1100

Periodical

Lecture notes in Electrical Engineering

Year of study

810

State

United Kingdom of Great Britain and Northern Ireland

Pages from

169

Pages to

178

Pages count

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"
}