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Found 20 papers, 14 papers with code
SE(3)-Equivariant and Noise-Invariant 3D Motion Tracking in Medical Images
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering
Preoperatively, a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT.
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series.
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero.
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
The placenta plays a crucial role in fetal development.
Boundary-weighted logit consistency improves calibration of segmentation networks
Neural network prediction probabilities and accuracy are often only weakly-correlated.
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units.
$E(3) \times SO(3)$-Equivariant Networks for Spherical Deconvolution in Diffusion MRI
We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an $E(3)\times SO(3)$ equivariant framework for sparse deconvolution of volumes where each voxel contains a spherical signal.
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains.
Data Consistent Deep Rigid MRI Motion Correction
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration
Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task.
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis
Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation.
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction
To these ends, we present a dual-domain self-supervised transformer (DSFormer) for accelerated MC-MRI reconstruction.
Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI
Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography.
Generative Adversarial Registration for Improved Conditional Deformable Templates
Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis.
Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort.
Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data
We then show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset.
Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization
Deep networks are now ubiquitous in large-scale multi-center imaging studies.
Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography
Optical Coherence Tomography (OCT) is pervasive in both the research and clinical practice of Ophthalmology.
Group Equivariant Generative Adversarial Networks
Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity.
