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Found 7 papers, 1 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.
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.
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.
