Medical image registration is vital for disease diagnosis and treatment with its ability to
merge diverse information of images, which may be captured under different times, angles …

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical
diagnosis and treatment. However, existing medical image segmentation methods exhibit …

Motivated by the rapid and current progress to develop intelligent image-guided intervention
tools, we aim in this paper to present, a recent review of a specific family of deep learning …

Deformable image registration aims to find a dense non-linear spatial correspondence
between a pair of images, which is a crucial step for many medical tasks such as tumor …

Deformable image registration is a fundamental step for medical image analysis. Recently
transformers have been used for registration and outperformed Convolutional Neural …

Deformable image registration is one of the essential processes in analyzing medical
images. In particular, when diagnosing abdominal diseases such as hepatic cancer and …

Recently, deep learning-based brain segmentation methods have achieved great success.
However, most approaches focus on supervised segmentation, which requires many high …

Deformable image registration plays a crucial role in medical imaging, aiding in disease
diagnosis and image-guided interventions. Traditional iterative methods are slow, while …

Deformable Image registration is a fundamental yet vital task for preoperative planning,
intraoperative information fusion, disease diagnosis and follow-ups. It solves the non-rigid …

Dense prediction is a fundamental requirement for many medical vision tasks such as
medical image restoration, registration, and segmentation. The most popular vision model …