Search Results for author:
Found 12 papers, 4 papers with code
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information.
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints.
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET
Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes.
U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD.
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.
VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation
The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry
Conclusion: Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.
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.
