Validation and evaluation metrics for medical and biomedical
image synthesis

C 2022

Validation and evaluation metrics for medical and biomedical image synthesis

NEČASOVÁ, Tereza, Ninon BURGOS and David SVOBODA

Basic information

Original name

Validation and evaluation metrics for medical and biomedical image synthesis

Authors

NEČASOVÁ, Tereza (203 Czech Republic, guarantor, belonging to the institution), Ninon BURGOS (250 France) and David SVOBODA (203 Czech Republic, belonging to the institution)

Edition

1st ed. Neuveden, Biomedical Image Synthesis and Simulation - Methods and Applications, p. 573-600, 28 pp. The MICCAI Society book Series, 2022

Publisher

Elsevier

Other information

Language

English

Type of outcome

Kapitola resp. kapitoly v odborné knize

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

RIV identification code

RIV/00216224:14330/22:00126257

Organization unit

Faculty of Informatics

ISBN

978-0-12-824349-7

DOI

http://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3

Keywords in English

Image synthesis; Image simulation; Similarity;Distance metrics;Image datasets

Abstract

V originále

Synthetic image data play an important role in the verification of medical and biomedical image analysis algorithms. However, the usage of such data strongly relies on their quality and plausibility. Despite the emergence of many frameworks for image synthesis in recent years, the quality of the generated images has not been sufficiently assessed in many cases, or the methodology varied across the publications. If we want to use synthetic image data for the verification of biomedical analysis tools, then the images should resemble the real ones as much as possible with evidence about their similarity. Initially, the hardware available for simulations was limited. Therefore, the validation was not under the scope of interest. With the technological improvements, the expectations put on synthetic data have arisen. Proper validation of synthetic image data is nowadays becoming essential.

Links

MUNI/A/1145/2021, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI. (Acronym: SV-FI MAV XI.)

Investor: Masaryk University

Citovat

NEČASOVÁ, Tereza, Ninon BURGOS and David SVOBODA. Validation and evaluation metrics for medical and biomedical image synthesis. In Ninon Burgos, David Svoboda. Biomedical Image Synthesis and Simulation - Methods and Applications. 1st ed. Neuveden: Elsevier, 2022, p. 573-600. The MICCAI Society book Series. ISBN 978-0-12-824349-7. Available from: https://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3.

@inbook{1874640,
   author = {Nečasová, Tereza and Burgos, Ninon and Svoboda, David},
   address = {Neuveden},
   booktitle = {Biomedical Image Synthesis and Simulation - Methods and Applications},
   doi = {http://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3},
   edition = {1st ed.},
   editor = {Ninon Burgos, David Svoboda},
   keywords = {Image synthesis; Image simulation; Similarity;Distance metrics;Image datasets},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-0-12-824349-7},
   pages = {573-600},
   publisher = {Elsevier},
   title = {Validation and evaluation metrics for medical and biomedical image synthesis},
   url = {http://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3},
   year = {2022}
}

TY  - CHAP
ID  - 1874640
AU  - Nečasová, Tereza - Burgos, Ninon - Svoboda, David
PY  - 2022
TI  - Validation and evaluation metrics for medical and biomedical image synthesis
VL  - The MICCAI Society book Series
PB  - Elsevier
CY  - Neuveden
SN  - 9780128243497
KW  - Image synthesis
KW  - Image simulation
KW  - Similarity;Distance metrics;Image datasets
UR  - http://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3
N2  - Synthetic image data play an important role in the verification of medical and biomedical image analysis algorithms. However, the usage of such data strongly relies on their quality and plausibility. Despite the emergence of many frameworks for image synthesis in recent years, the quality of the generated images has not been sufficiently assessed in many cases, or the methodology varied across the publications. If we want to use synthetic image data for the verification of biomedical analysis tools, then the images should resemble the real ones as much as possible with evidence about their similarity. Initially, the hardware available for simulations was limited. Therefore, the validation was not under the scope of interest. With the technological improvements, the expectations put on synthetic data have arisen. Proper validation of synthetic image data is nowadays becoming essential.
ER  -

NEČASOVÁ, Tereza, Ninon BURGOS and David SVOBODA. Validation and evaluation metrics for medical and biomedical image synthesis. In Ninon Burgos, David Svoboda. \textit{Biomedical Image Synthesis and Simulation - Methods and Applications}. 1st ed. Neuveden: Elsevier, 2022, p.~573-600. The MICCAI Society book Series. ISBN~978-0-12-824349-7. Available from: https://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3.

Detailed Information on Publication Record