Automatic 3D Tracking of Liver Metastases: Follow-Up Assessment of Cancer Patients in Contrast-Enhanced MRI
We automated segmentation and quantification of individual NELMs, enabling comprehensive longitudinal analysis of differential tumor growth with the potential to enhance clinical decision-making.
Authors
- Tobias Penzkofer (ORCID: https://orcid.org/0000-0001-9591-8575)
- Richard R. Ruppel (ORCID: https://orcid.org/0009-0002-7081-0300)
- Georg Lukas Baumgaertner (ORCID: https://orcid.org/0000-0003-3046-0019)
- Bertram Wiedenmann (ORCID: https://orcid.org/0000-0002-7890-2552)
- Martina T. Mogl (ORCID: https://orcid.org/0000-0002-3676-047X)
- Dominik Geisel (ORCID: https://orcid.org/0000-0001-7348-0971)
- Johannes Kolck (ORCID: https://orcid.org/0000-0001-9843-2247)
- Maximilian Lindholz (ORCID: https://orcid.org/0000-0003-3690-1161)
- Henning Jann (ORCID: https://orcid.org/0000-0003-4095-336X)
- Sophia Schulze‐Weddige (ORCID: https://orcid.org/0000-0003-4248-5563)
- Isabel Schobert (ORCID: https://orcid.org/0000-0003-3659-0914)
- Uli Fehrenbach (ORCID: https://orcid.org/0000-0003-3622-3268)
- Anna-Maria Haack
Institutions
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-08-12
- DOI
- https://doi.org/10.3390/bioengineering12080874
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00