477 citations · 660 across the 7 of their papers we have counts for
7 papers
Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
Veronika A. Zimmer, Alberto Gomez, Emily Skelton +10
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in…
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation
Qianye Yang, Tom Vercauteren, Yunguan Fu +7
Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the vol…
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Qianye Yang, Yunguan Fu, Francesco Giganti +6
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance progra…
Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time
Cesare Magnetti, Veronika Zimmer, Nooshin Ghavami +6
We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a fu…
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
Yipeng Hu, Marc Modat, Eli Gibson +11
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a…
Adversarial Deformation Regularization for Training Image Registration Neural Networks
Yipeng Hu, Eli Gibson, Nooshin Ghavami +6
We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fiel…
Label-driven weakly-supervised learning for multimodal deformable image registration
Yipeng Hu, Marc Modat, Eli Gibson +7
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propo…