8 papers
CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration
Eytan Kats, Christoph Grossbroehmer, Ziad Al-Haj Hemidi +3
Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsiste…
Effective Feature Learning for 3D Medical Registration via Domain-Specialized DINO Pretraining
Eytan Kats, Mattias P. Heinrich
Medical image registration is a critical component of clinical imaging workflows, enabling accurate longitudinal assessment, multi-modal data fusion, and image-guided interventions…
Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow
Eytan Kats, Kai Geissler, Daniel Mensing +4
In clinical radiology, accurate patient table positioning is essential to align specific internal organs of interest with the scanner imaging isocenter, ensuring image quality and…
PrIINeR: Towards Prior-Informed Implicit Neural Representations for Accelerated MRI
Ziad Al-Haj Hemidi, Eytan Kats, Mattias P. Heinrich
Accelerating Magnetic Resonance Imaging (MRI) reduces scan time but often degrades image quality. While Implicit Neural Representations (INRs) show promise for MRI reconstruction,…
Internal Organ Localization Using Depth Images
Eytan Kats, Kai Geißler, Jochen G. Hirsch +2
Automated patient positioning is a crucial step in streamlining MRI workflows and enhancing patient throughput. RGB-D camera-based systems offer a promising approach to automate th…
Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation
Eytan Kats, Jochen G. Hirsch, Mattias P. Heinrich
This paper demonstrates a self-supervised framework for learning voxel-wise coarse-to-fine representations tailored for dense downstream tasks. Our approach stems from the observat…
A Soft STAPLE Algorithm Combined with Anatomical Knowledge
Eytan Kats, Jacob Goldberger, Hayit Greenspan
Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the exp…
Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation
Eytan Kats, Jacob Goldberger, Hayit Greenspan
This paper explores the use of a soft ground-truth mask ("soft mask'') to train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple Sclerosis (MS) lesions. Det…