Diffusion Probabilistic Models (DPMs) have recently been employed for image deblurring.
Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task.
Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation.
Our results show improved performance in counting and localization of objects in 2D and 3D microscopy data.
Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography.
Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis.
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort.
We then show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset.
Optical Coherence Tomography (OCT) is pervasive in both the research and clinical practice of Ophthalmology.