Search Results for author:
Found 24 papers, 9 papers with code
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision.
Framing image registration as a landmark detection problem for better representation of clinical relevance
Nowadays, registration methods are typically evaluated based on sub-resolution tracking error differences.
Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks
We introduce a novel Bayesian neural network-based architecture to estimate inter-rater uncertainty in medical image segmentation.
Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
Even though simultaneous optimization of similarity metrics represents a standard procedure in the field of semantic segmentation, surprisingly, this does not hold true for image registration.
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
We then regress the severity of the fracture as a function of the distance to this hyperplane, calibrating the results to the Genant scale.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI.
Interpretable Vertebral Fracture Diagnosis
Do black-box neural network models learn clinically relevant features for fracture diagnosis?
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach.
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach.
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.
Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging
This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging.
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
The first subnetwork is designed to complete the shape of the downsampled defective skull.
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme.
Inferring the 3D Standing Spine Posture from 2D Radiographs
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).
Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy
Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net.
A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Robust localisation and identification of vertebrae is essential for automated spine analysis.
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.
