Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …

We introduce a strategy for learning image registration without acquired imaging data,
producing powerful networks agnostic to contrast introduced by magnetic resonance …

Image synthesis learns a transformation from the intensity features of an input image to yield
a different tissue contrast of the output image. This process has been shown to have …

Existing self-supervised medical image segmentation usually encounters the domain shift
problem (ie, the input distribution of pre-training is different from that of fine-tuning) and/or …

Diffusion magnetic resonance images typically suffer from spatial distortions due to
susceptibility induced off-resonance fields, which may affect the geometric fidelity of the …

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and
radiotherapy treatment planning. Since CT image intensities are directly related to positron …

Recently, more and more attention is drawn to the field of medical image synthesis across
modalities. Among them, the synthesis of computed tomography (CT) image from T1 …

An important task in image processing and neuroimaging is to extract quantitative
information from the acquired images in order to make observations about the presence of …

Medical image acquisition plays a significant role in the diagnosis and management of
diseases. Magnetic Resonance (MR) and Computed Tomography (CT) are considered two …

Different modalities of magnetic resonance imaging (MRI) can indicate tumor-induced tissue
changes from different perspectives, thus benefit brain tumor segmentation when they are …