Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the
All modalities were aligned to IMS data on a per pixel basis, by linking of the theoretical pixel location in each image to the laser ablation marks made by the IMS laser in a raster across the tissue
Source shapes were randomly generated from from a mesh model of a human femur (Fig. 1B), misaligned by [10, 20] mm / degrees, and registered back to a point-cloud representation of the mesh. The test
Analyzed data from the serpentine Doppler phantom. The variable Acq consists of structures of the image data as they were originally acquired, and Registered moves each dataset from Acq to their respe
This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods andunsupervised deep-learning approaches for non-rigid registration, particularly emphasi
