2 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
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,…
Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023
M. A. D. Buser, D. C. Simons, M. Fitski +27
Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magnetic resonance imaging (MRI)-based…
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…
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…