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Multimodal medical volumetric image fusion using 3-D Shearlet transform and T-S fuzzy reasoning
Multimodal medical volumetric image fusion is a hot topic in medical image processing. Currently, most multi-scale based medical image fusion methods...
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Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis
Regular group convolutional neural networks (G-CNNs) have been shown to increase model performance and improve equivariance to different geometrical...
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Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations
Image segmentation using weak annotations like scribbles has gained great attention, since such annotations are easier to obtain compared to...
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A Deeper Analysis of Volumetric Relightable Faces
Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive...
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Volumetric structure extraction in a single image
High-level structure (HLS) extraction recovers 3D elements on human-made surfaces (objects, buildings, ground, etc.). There are several approaches to...
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Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep...
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A Hybrid Propagation Network for Interactive Volumetric Image Segmentation
Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional...
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Weakly Supervised Volumetric Image Segmentation with Deformed Templates
There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric...
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KeypointNeRF: Generalizing Image-Based Volumetric Avatars Using Relative Spatial Encoding of Keypoints
Image-based volumetric humans using pixel-aligned features promise generalization to unseen poses and identities. Prior work leverages global spatial...
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Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency
Computer-aided clinical decision support tools for radiology often suffer from poor generalizability in multi-centric frameworks due to data...
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Implicit U-Net for Volumetric Medical Image Segmentation
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net...
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VTP: volumetric transformer for multi-view multi-person 3D pose estimation
This paper presents Volumetric Transformer Pose Estimator (VTP), the first 3D volumetric transformer framework for multi-view multi-person 3D human...
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CardiacSeg: Customized Pre-training Volumetric Transformer with Scaling Pyramid for 3D Cardiac Segmentation
Congenital heart disease (CHD) is the most common type of birth defect and a leading cause of death worldwide. The volumetric segmentation of the...
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VolPAM: Volumetric Phenotype-Activation-Map for data-driven discovery of 3D imaging phenotypes and interpretability
Knowledge about the subtypes of a disease critically affects clinical decisions ranging from the choice of therapeutic options to patient management....
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A More Design-Flexible Medical Transformer for Volumetric Image Segmentation
UNet-based encoder-decoder networks dominate volumetric medical image segmentation in the past several years. Many improvements focus on the design...
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Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data
Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with...
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AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture
To address the ill-posed problem caused by partial observations in monocular human volumetric capture, we present AvatarCap, a novel framework that...
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Abstracting Volumetric Medical Images with Sparse Keypoints for Efficient Geometric Segmentation of Lung Fissures with a Graph CNN
Volumetric image segmentation often relies on voxel-wise classification using 3D convolutional neural networks (CNNs). However, 3D CNNs are...
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A Deep Approach for Volumetric Tractography Segmentation
The study of tracts—bundles of nerve fibers that are organized together and have a similar function—is of major interest in neurology and related...