38 citations · 63 across the 5 of their papers we have counts for
7 papers
A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy
Laurens Beljaards, Mohamed S. Elmahdy, Fons Verbeek +1
Recently, joint registration and segmentation has been formulated in a deep learning setting, by the definition of joint loss functions. In this work, we investigate joining these…
Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT
Mohamed S. Elmahdy, Tanuj Ahuja, U. A. van der Heide +1
Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning. In an adaptive radiotherapy setting, updated contours need to be gen…
Adaptive-CS-Net: FastMRI with Adaptive Intelligence
Nicola Pezzotti, Elwin de Weerdt, Sahar Yousefi +9
Adaptive intelligence aims at empowering machine learning techniques with the extensive use of domain knowledge. In this work, we present the application of adaptive intelligence t…
Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network
Sahar Yousefi, Lydiane Hirschler, Merlijn van der Plas +4
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…
3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations
Hessam Sokooti, Bob de Vos, Floris Berendsen +5
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.…
Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy
Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti +2
Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversaria…