From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
Deep learning-based medical image-to-mesh reconstruction has rapidly evolved,
enabling the transformation of medical imaging data into three-dimensional mesh
models that are critical in computational medicine and in silico trials for
advancing our understanding of disease mechanisms, and diagnostic and
therapeutic techniques in modern medicine. This survey systematically
categorizes existing approaches into four main categories: template models,
statistical models, generative models, and implicit models. Each category is
analysed in detail, examining their methodological foundations, strengths,
limitations, and applicability to different anatomical structures and imaging
modalities. We provide an extensive evaluation of these methods across various
anatomical applications, from cardiac imaging to neurological studies,
supported by quantitative comparisons using standard metrics. Additionally, we
compile and analyze major public datasets available for medical mesh
reconstruction tasks and discuss commonly used evaluation metrics and loss
functions. The survey identifies current challenges in the field, including
requirements for topological correctness, geometric accuracy, and
multi-modality integration. Finally, we present promising future research
directions in this domain. This systematic review aims to serve as a
comprehensive reference for researchers and practitioners in medical image
analysis and computational medicine.
