With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the
detection of prostate cancer have enabled its integration into clinical routines in the past two …

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 …

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential
importance for image-guided prostate interventions and treatment planning. However …

Spatially aligning medical images from different modalities remains a challenging task,
especially for intraoperative applications that require fast and robust algorithms. We propose …

Automatic prostate segmentation in transrectal ultrasound (TRUS) is of essential importance
for image-guided prostate biopsy and treatment planning. However, developing such …

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and
its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such …

Background. Celiac disease is one of the most common diseases in the world. Capsule
endoscopy is an alternative way to visualize the entire small intestine without invasiveness …

Abstract A non-rigid MR-TRUS image registration framework is proposed for prostate
interventions. The registration framework consists of a convolutional neural networks (CNN) …

We describe an adversarial learning approach to constrain convolutional neural network
training for image registration, replacing heuristic smoothness measures of displacement …