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Found 23 papers, 11 papers with code
Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields
In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code.
Seg-metrics: a Python package to compute segmentation metrics
In response to a concerning trend of selectively emphasizing metrics in medical image segmentation (MIS) studies, we introduce \texttt{seg-metrics}, an open-source Python package for standardized MIS model evaluation.
CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation
In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation.
Joint optimization of a $β$-VAE for ECG task-specific feature extraction
Electrocardiography is the most common method to investigate the condition of the heart through the observation of cardiac rhythm and electrical activity, for both diagnosis and monitoring purposes.
Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapy
However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions.
Prediction of Lung CT Scores of Systemic Sclerosis by Cascaded Regression Neural Networks
We used 227 3D CT scans to train and validate the first network, and the resulting 1135 axial slices were used in the second network.
Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer
In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information.
Multi-task Semi-supervised Learning for Pulmonary Lobe Segmentation
Deep multi-task learning is expected to utilize labels of multiple different structures.
ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network
To tackle this problem, we present a new semi-supervised multitask CNN which is trained on both paired data, i. e. ASL and PET scans, and unpaired data, i. e. only ASL scans, which alleviates the problem of training a network on limited paired data.
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)
The proposed network achieved a $\mathrm{DSC}$ value of $0. 79 \pm 0. 20$, a mean surface distance of $5. 4 \pm 20. 2mm$ and $95\%$ Hausdorff distance of $14. 7 \pm 25. 0mm$ for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone.
A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy
The obtained performance as well as the inference speed make this a promising candidate for daily re-contouring in adaptive radiotherapy, potentially reducing treatment-related side effects and improving quality-of-life after treatment.
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
In this work, we present the application of adaptive intelligence to accelerate MR acquisition.
Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT
We investigate a transfer learning approach, fine-tuning the baseline CNN model to a specific patient, based on imaging acquired in earlier treatment fractions.
Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation
In conclusion, the proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.
Ranked #1 on
Pulmonary Artery–Vein Classification
on SunYs
3D Medical Imaging Segmentation
Pulmonary Artery–Vein Classification
3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.
Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network
Hadamard time-encoded pseudo-continuous arterial spin labeling (te-pCASL) is a signal-to-noise ratio (SNR)-efficient MRI technique for acquiring dynamic pCASL signals that encodes the temporal information into the labeling according to a Hadamard matrix.
Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy
Joint image registration and segmentation has long been an active area of research in medical imaging.
Quantitative Error Prediction of Medical Image Registration using Regression Forests
This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans.
Pulmonary vessel tree matching for quantifying changes in vascular morphology
Quantifying morphological changes may provide a non-invasive assessment of treatment effects in CTEPH patients, consistent with hemodynamic changes from invasive RHC.
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration.
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
In this work we propose a deep learning network for deformable image registration (DIRNet).
A Novel Motion Detection Method Resistant to Severe Illumination Changes
Recently, there has been a considerable attention given to the motion detection problem due to the explosive growth of its applications in video analysis and surveillance systems.
