The field of medical imaging and geometry processing is rapidly advancing, with a focus on developing innovative methods for image segmentation, registration, and analysis. Recent research has led to the development of new deep learning models and algorithms that can accurately segment and register medical images, including those with complex anatomical structures. One of the key directions in this field is the use of geometric deep learning techniques, which can effectively capture the spatial relationships and structures present in medical images. These techniques have been applied to a range of tasks, including image segmentation, registration, and shape analysis. Another important area of research is the development of methods for deformable image registration, which can be used to align images of the same scene or object taken at different times or from different viewpoints. This has numerous applications in medical imaging, including the tracking of tissue growth or change over time. Notable papers in this area include TissUnet, which presents a deep learning model for segmenting extracranial tissues from brain MRI scans, and DeformCL, which introduces a new deformable centerline representation for vessel extraction in 3D medical images. Other noteworthy papers include UFM, which develops a unified flow and matching model for dense image correspondence, and CINeMA, which presents a novel framework for creating high-resolution, spatio-temporal, multimodal brain atlases. These advances have the potential to improve our understanding of complex medical phenomena and enable the development of more effective diagnostic and therapeutic strategies.