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 a David SVOBODA

Základní údaje

Originální název

Validation and evaluation metrics for medical and biomedical image synthesis

Autoři

NEČASOVÁ, Tereza (203 Česká republika, garant, domácí), Ninon BURGOS (250 Francie) a David SVOBODA (203 Česká republika, domácí)

Vydání

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

Nakladatel

Elsevier

Další údaje

Jazyk

angličtina

Typ výsledku

Kapitola resp. kapitoly v odborné knize

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Kód RIV

RIV/00216224:14330/22:00126257

Organizační jednotka

Fakulta informatiky

ISBN

978-0-12-824349-7

DOI

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

Klíčová slova anglicky

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

Štítky

cbia-web

Příznaky

Mezinárodní význam

Změněno: 28. 3. 2023 11:40, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/1145/2021, interní kód MU

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

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI.

Citovat

NEČASOVÁ, Tereza, Ninon BURGOS a 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, s. 573-600. The MICCAI Society book Series. ISBN 978-0-12-824349-7. Dostupné z: 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 a 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, s.~573-600. The MICCAI Society book Series. ISBN~978-0-12-824349-7. Dostupné z: https://dx.doi.org/10.1016/B978-0-12-824349-7.00032-3.

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