Introduced in 1993, the DIRECT global optimization algorithm provided a fresh approach to
minimizing a black-box function subject to lower and upper bounds on the variables. In …

The frontiers of bioimaging are currently being pushed toward the integration and correlation
of several modalities to tackle biomedical research questions holistically and across multiple …

Image registration is a fundamental medical image analysis task, and a wide variety of
approaches have been proposed. However, only a few studies have comprehensively …

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

Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as
imaging, text, and physiological signals, and can be applied in various fields. In the medical …

Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

A popular benchmark for intra-patient lung registration is provided by the DIR-LAB
COPDgene dataset consisting of large-motion in-and expiratory breath-hold CT pairs. This …

Establishing dense anatomical correspondence across distinct imaging modalities is a
foundational yet challenging procedure for numerous medical image analysis studies and …

Methods for deep learning based medical image registration have only recently approached
the quality of classical model-based image alignment. The dual challenge of both a very …

Multi-modal registration is a required step for many image-guided procedures, especially
ultrasound-guided interventions that require anatomical context. While a number of such …