Noninvasive medical neuroimaging has yielded many discoveries about the brain
connectivity. Several substantial techniques mapping morphological, structural and …

Automated computed tomography (CT) scan segmentation (labelling of pixels according to
tissue type) is now possible. This technique is being adapted to achieve three‐dimensional …

Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …

Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting
various anatomical structures in medical images but often suffer from relatively poor …

Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used
to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide …

Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …

The lack of annotated medical images limits the performance of deep learning models,
which usually need large-scale labelled datasets. Few-shot learning techniques can reduce …

Data for healthcare is diverse and includes many different modalities. Traditional
approaches to Artificial Intelligence for cardiovascular disease were typically limited to …

In this work, we address the task of few-shot medical image segmentation (MIS) with a novel
proposed framework based on the learning registration to learn segmentation (LRLS) …