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Daniel Rueckert

44 papers hereh-index 14724 citations80 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author37
  • last author5

Across the 42 of 44 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • eess.IV10
  • cs.LG5
  • physics.med-ph3
  • cs.AI1
same name
  • Daniel Rueckert — 12 papers, h 4
  • Daniel Rueckert — 7 papers, h 10
  • Daniel Rueckert — 5 papers, h 4
  • Daniel Rueckert — 5 papers, h 4
  • Daniel Rueckert — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

works on
angiography 1benchmark challenge 1circle of willis segmentation 1deep learning 1vascular imaging 1

From the 1 of 44 linked papers with an AI index.

activity
20242026
most citedThe TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

12 citations · 13 across the 14 of their papers we have counts for

collaborators
Showing physics.med-phShow all

3 papers · 1 filter

physics.med-ph2025

From Fiber Tracts to Tumor Spread: Biophysical Modeling of Butterfly Glioma Growth Using Diffusion Tensor Imaging

Jonas Weidner, Ivan Ezhov, Michal Balcerak +5

Butterfly tumors are a distinct class of gliomas that span the corpus callosum, producing a characteristic butterfly-shaped appearance on MRI. The distinctive growth pattern of the…

physics.med-ph2025

Redefining spectral unmixing for in-vivo brain tissue analysis from hyperspectral imaging

Martin Hartenberger, Huzeyfe Ayaz, Fatih Ozlugedik +12

In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To…

physics.med-ph2024

A Learnable Prior Improves Inverse Tumor Growth Modeling

Jonas Weidner, Ivan Ezhov, Michal Balcerak +11

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. H…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.