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 analyse 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.

The brain tumor is one of the primary diseases that endanger human life and health. Multi-modality magnetic resonance imaging (MRI) for tumor analysis is one of the key techniques for clinical diagnosis. Experienced experts artificially segment the classical method of brain tumor segmentation according to their anatomical and pathological knowledge, which is time-consuming. In this paper, we propose a novel deep neural network structure, namely Feature Mining Networks (FMNet), for brain tumor segmentation. The proposed FMNet adopts three innovative structure, including semantic information mining unit (SIMU), macro information mining unit (MIMU), and a feature correction unit (FCU). These three units can enhance the mining of semantic information and spatial information, and further, modify the information in a direction that is conducive to segmentation results. Each unit can bring significant improvement in segmentation performance. We evaluate the proposed framework on BraTS2017 and BraTS2018 dataset. The experimental results show that our FMNet performs better than state-of-the-art networks such as fully convolutional networks (FCN), U-Net, VGG, and Hybrid Pyramid U-Net (HPUNet).