77 citations
- Harvard UniversityUS11 papers
- Brigham and Women's HospitalUS9 papers
- Harvard University PressUS7 papers
- Maastricht UniversityNL7 papers
- Dana-Farber Brigham Cancer CenterUS5 papers
- Charité - Universitätsmedizin BerlinDE4 papers
- Dana-Farber Cancer InstituteUS4 papers
- Humboldt-Universität zu BerlinDE4 papers
- Massachusetts Institute of TechnologyUS4 papers
- Technical University of MunichDE4 papers
- Boston Children's HospitalUS3 papers
- Cornell UniversityUS3 papers
5 papers · 1 filter
Improving Quality Control Of MRI Images Using Synthetic Motion Data
Charles Bricout, Kang Ik K. Cho, Michael Harms +10
MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To add…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Yining Zha, Benjamin H. Kann, Zezhong Ye +8
Introduction: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. How…
Transfer Learning from an Artificial Radiograph-landmark Dataset for Registration of the Anatomic Skull Model to Dual Fluoroscopic X-ray Images
Chaochao Zhou, Thomas Cha, Yun Peng +1
Registration of 3D anatomic structures to their 2D dual fluoroscopic X-ray images is a widely used motion tracking technique. However, deep learning implementation is often impeded…
Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology
Christopher P. Bridge, Chris Gorman, Steven Pieper +6
Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperabil…