Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

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

Like other applications in computer vision, medical image segmentation and his email
address have been most successfully addressed using deep learning models that rely on …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Foetal MRI is a complementary imaging method to antenatal ultrasound. It provides
advanced information for detection and characterisation of foetal brain and body anomalies …

Brain segmentation is often the first and most critical step in quantitative analysis of the brain
for many clinical applications, including fetal imaging. Different aspects challenge the …

Deep learning models represent the state of the art in medical image segmentation. Most of
these models are fully-convolutional networks (FCNs), namely each layer processes the …

Abstract Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to
study the fetal brain in utero. An important computation enabled by dMRI is streamline …

Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is
a lack of universally accepted protocols for fetal brain parcellation and segmentation …