69 citations · 126 across the 27 of their papers we have counts for
10 papers · 1 filter
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
Moritz Rempe, Lukas Heine, Constantin Seibold +2
Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection…
Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT
Hamza Kalisch, Fabian Hörst, Ken Herrmann +2
Lesion segmentation in PET/CT imaging is essential for precise tumor characterization, which supports personalized treatment planning and enhances diagnostic precision in oncology.…
Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods
Zdravko Marinov, Moon Kim, Jens Kleesiek +1
Interactive segmentation plays a crucial role in accelerating the annotation, particularly in domains requiring specialized expertise such as nuclear medicine. For example, annotat…
Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
Matthias Hadlich, Zdravko Marinov, Moon Kim +3
Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations.…
Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines
Alexander Jaus, Constantin Seibold, Kelsey Hermann +5
In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided…
Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling
Constantin Seibold, Alexander Jaus, Matthias A. Fink +5
Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we prop…
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging
Zdravko Marinov, Simon Reiß, David Kersting +2
Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors. PET/CT segmentation models can automate tumor delineation, however, cu…
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
Lars Heiliger, Zdravko Marinov, Max Hasin +9
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-sta…
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation
Constantin Seibold, Simon Reiß, Jens Kleesiek +1
Producing densely annotated data is a difficult and tedious task for medical imaging applications. To address this problem, we propose a novel approach to generate supervision for…
Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
Constantin Seibold, Matthias A. Fink, Charlotte Goos +4
Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information. From this spectral data, dif…