10 citations · 30 across the 23 of their papers we have counts for
7 papers · 1 filter
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
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.…
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Zdravko Marinov, Paul F. Jäger, Jan Egger +2
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback ta…
AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net
Matthias Hadlich, Zdravko Marinov, Rainer Stiefelhagen
Tumor segmentation in medical imaging is crucial and relies on precise delineation. Fluorodeoxyglucose Positron-Emission Tomography (FDG-PET) is widely used in clinical practice to…
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…