Slice to Volume Registration

Slice-to-volume registration (SVR) aims to accurately align individual 2D image slices with a corresponding 3D volume, a crucial step in various medical imaging applications. Current research emphasizes developing robust and efficient SVR methods using deep learning architectures, such as transformers and convolutional neural networks, often incorporating self-attention mechanisms to handle noisy data and variations in slice quality. These advancements are improving the accuracy and speed of registration across modalities like ultrasound, fMRI, and OCT, leading to better image quality for diagnosis and quantitative analysis in clinical settings. The development of user-friendly annotation tools further facilitates the creation of benchmark datasets, accelerating algorithm development and validation.