Medical image fusion (MIF) plays an important role in precision diagnostics and treatment planning management, and medical image fusion quality assessment (MIFQA) has an aggressive effect in improving MIF performance. However, obtaining medical reference images is difficult, and the significant demand for medical prior knowledge and reference images is an important challenge in the field of MIFQA.

3D tubular objects have complex spatial shapes. Direct volume visualization cannot intuitively display their morphological characteristics and surface abnormalities. Straightening reformation is an effective visualization method for tubular objects. It uses a swept frame to sample cross sections along the centerline of the tubular object to generate straightening result. So far, however, current methods

Recently, combining the strategy of consistency regularization with uncertainty estimation has shown promising performance on semi-supervised medical image segmentation tasks. However, most existing methods estimate the uncertainty solely based on the outputs of a single neural network, which results in imprecise uncertainty estimations and eventually degrades the segmentation performance. In this

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework

Multi-contrast magnetic resonance imaging (MC-MRI) plays a crucial role in clinical practice. However, its performance is hindered by long scanning times and the isolation between image acquisition and downstream clinical diagnoses/treatments. Despite the activated research on accelerated MC-MRI, few existing studies prioritize personalized imaging tailored to individual patient characteristics and

Medical image registration is a crucial process for aligning anatomical structures, enabling applications such as atlas mapping, longitudinal analysis, and multimodal data fusion. This paper introduces DINO-Reg, an adaptation-free registration method leveraging the vision foundation model, DINOv2, to extract features for deformable 3D medical image alignment. Although DINOv2 was originally trained

The translation of imaging Mueller polarimetry to clinical practice is often hindered by large footprint and relatively slow acquisition speed of the existing instruments. Using polarization-sensitive camera as a detector may reduce instrument dimensions and allow data streaming at video rate. However, only the first three rows of a complete 4×4 Mueller matrix can be measured. To overcome this hurdle