65 citations · 104 across the 30 of their papers we have counts for
22 papers · 1 filter
Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans
Benjamin Hamm, Nico Albert Disch, Maximilian Rokuss +3
Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents raises safety concerns and adds…
Feature Space Guidance for Breast Cancer Classification in DCE-MRI
Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss +6
Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail together with rich temporal cont…
nnFoundation: 3D Foundation Models for Radiology
Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…
Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation
Jonathan Suprijadi, Raphael Stock, Moritz Langenberg +10
Vision-language models offer a promising path toward automating radiology report generation, but applying them to full 3D CT volumes poses substantial computational challenges. Mod…
Rethinking Post-Hoc Calibration in Semantic Segmentation
Tristan Kirscher, Kim-Celine Kahl, Balint Kovacs +5
Reliable confidence estimates are essential in semantic segmentation, yet modern models often remain miscalibrated. We investigate two overlooked issues in post-hoc calibration. Fi…
Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking
Yannick Kirchhoff, Maximilian Rokuss, Daniel Philipp Mertens +5
Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-off: end-to-end trackers achieve…
Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation
Tristan Kirscher, Markus Bujotzek, Yannick Kirchhoff +5
Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV),…
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62
Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings acro…
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Jakob Dexl, Katharina Jeblick, Andreas Mittermeier +27
We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generaliza…
CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series
Nico Albert Disch, Saikat Roy, Constantin Ulrich +5
Forecasting how 3D medical scans evolve over time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a singl…
VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation
Maximilian Rokuss, Moritz Langenberg, Yannick Kirchhoff +8
We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences,…
MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI
Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss +1
The ODELIA Breast MRI Challenge 2025 addresses a critical issue in breast cancer screening: improving early detection through more efficient and accurate interpretation of breast M…
A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
Omer Faruk Durugol, Maximilian Rokuss, Yannick Kirchhoff +1
Automated segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) from MRI is critical for clinical workflows but is hindered by poor tumor-tissue contrast and a scarcity of annota…
Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models
Maximilian Rokuss, Yannick Kirchhoff, Fabian Isensee +1
Whole-body PET/CT is a cornerstone of oncological imaging, yet accurate lesion segmentation remains challenging due to tracer heterogeneity, physiological uptake, and multi-center…
Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging
Nico Albert Disch, Yannick Kirchhoff, Robin Peretzke +5
Understanding temporal dynamics in medical imaging is crucial for applications such as disease progression modeling, treatment planning and anatomical development tracking. However…
nnInteractive: Redefining 3D Promptable Segmentation
Fabian Isensee, Maximilian Rokuss, Lars Krämer +10
Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation models like SAM have revolutionized interactive segmentation, thei…
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal +7
In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capab…
Expectation-Maximization as the Engine of Scalable Medical Intelligence
Wenxuan Li, Pedro R. A. S. Bassi, Tianyu Lin +19
Large, high-quality, annotated datasets are the foundation of medical AI research, but constructing even a small, moderate-quality, annotated dataset can take years of effort from…
Scaling nnU-Net for CBCT Segmentation
Fabian Isensee, Yannick Kirchhoff, Lars Kraemer +3
This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the…
RadioActive: 3D Radiological Interactive Segmentation Benchmark
Constantin Ulrich, Tassilo Wald, Emily Tempus +3
Effortless and precise segmentation with minimal clinician effort could greatly streamline clinical workflows. Recent interactive segmentation models, inspired by METAs Segment Any…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…