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Found 31 papers, 5 papers with code
Reference-Free Multi-Modality Volume Registration of X-Ray Microscopy and Light-Sheet Fluorescence Microscopy
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as two pivotal imaging tools in preclinical research on bone remodeling diseases, offering micrometer-level resolution.
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
However, the choice of loss function profoundly affects the reconstructed images.
Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization
The average organ dice of the proposed method is 0. 71 compared with 0. 63 in baseline model, indicating the enhancement of anatomical structures.
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration
Incremental transfer learning, which combines peer-to-peer federated learning and domain incremental learning, can overcome the data privacy issue and meanwhile preserve model performance by using continual learning techniques.
OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios
The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i. e., Multiple Networks Overlaying (MNetO).
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding
The StyleGAN2 is then used to embed the photographs to its highly expressive latent space.
BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction
Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields.
Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology
The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam.
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Conclusions: The Segment Anything foundation model, while trained on photos, can achieve high zero-shot accuracy for glioma brain tumor segmentation on MRI slices.
Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis.
Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning
We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment.
Metal-conscious Embedding for CBCT Projection Inpainting
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images.
Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy
For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations.
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification
With iterative continual learning (i. e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0. 914, which is identical to the sensitivity using mixed data for training.
Learning Perspective Deformation in X-Ray Transmission Imaging
The complementary view setting provides a practical way to identify perspectively deformed structures by assessing the deviation between the two views.
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.
Deep learning for brain metastasis detection and segmentation in longitudinal MRI data
To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels.
Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery
For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed.
Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization
For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction.
Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health.
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems
The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93. 0% and 80. 7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections respectively.
Appearance Learning for Image-based Motion Estimation in Tomography
To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach.
Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images
For example, for truncated data, DCR achieves a mean root-mean-square error of 24 HU and a mean structure similarity index of 0. 999 inside the field-of-view for different patients in the noisy case, while the state-of-the-art U-Net method achieves 55 HU and 0. 995 respectively for these two metrics.
Projection Inpainting Using Partial Convolution for Metal Artifact Reduction
In this work, partial convolution is applied for projection inpainting, which only relies on valid pixels values.
Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning
Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images.
Superpixel-Based Background Recovery from Multiple Images
Background candidate images are obtained from input raw images with the masks.
Field of View Extension in Computed Tomography Using Deep Learning Prior
In computed tomography (CT), data truncation is a common problem.
Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.
Scale-Space Anisotropic Total Variation for Limited Angle Tomography
Hence, the main purpose of this paper is to reduce streak artifacts at various scales.
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction
Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements.
