Detail publikačního výsledku

Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

NOHEL, M.; ULRICH, C.; SUPRIJADI, J.; WALD, T.; MAIER-HEIN, K.

Originální název

Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

Anglický název

Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstruction-based SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the toolkit’s efficacy in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://github.com/MIC-DKFZ/Foreground-and-Anonymization-Area-Segmentation.

Anglický abstrakt

This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstruction-based SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the toolkit’s efficacy in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://github.com/MIC-DKFZ/Foreground-and-Anonymization-Area-Segmentation.

Klíčová slova

segmentation

Klíčová slova v angličtině

segmentation

Autoři

NOHEL, M.; ULRICH, C.; SUPRIJADI, J.; WALD, T.; MAIER-HEIN, K.

Vydáno

02.03.2025

Nakladatel

Springer Vieweg

Místo

Wiesbaden

ISBN

978-3-658-47421-8

Kniha

Bildverarbeitung für die Medizin 2025

Periodikum

Informatik aktuell

Stát

Spolková republika Německo

Strany od

242

Strany do

247

Strany počet

6

URL

https://link.springer.com/chapter/10.1007/978-3-658-47422-5_53

BibTex

@inproceedings{BUT197344,
  author="Michal {Nohel} and Constantin {Ulrich} and Jonathan {Suprijadi} and Tassilo {Wald} and Klaus {Maier-Hein}",
  title="Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data",
  booktitle="Bildverarbeitung für die Medizin 2025",
  year="2025",
  journal="Informatik aktuell",
  pages="242--247",
  publisher="Springer Vieweg",
  address="Wiesbaden",
  doi="10.1007/978-3-658-47422-5\{_}53",
  isbn="978-3-658-47421-8",
  issn="1431-472X",
  url="https://link.springer.com/chapter/10.1007/978-3-658-47422-5_53"
}