Search Results for author:
Found 8 papers, 4 papers with code
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.
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.
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.
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.
