用可变形模板提升医学图像网格重建精度
Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction
- 根据影像自动生成个性化模板，再进行形变重建
- 在OASIS数据集上达到0.267mm平均对称表面距离
- 适用于多种影像模态和解剖结构，通用性强
网格重建在计算机模拟、数字孪生、手术规划与导航等应用中至关重要。深度学习虽显著提升了重建速度，但传统方法多依赖固定模板进行个体形变，忽视解剖差异，影响重建质量。本文提出自适应模板网格重建网络（ATMRN），从输入影像生成个性化模板，再进行形变，突破单一模板限制。在OASIS数据集的皮层磁共振影像上验证，该方法在四类皮层结构上的平均对称表面距离达0.267mm，创下新基准。方法具备通用性，可轻松迁移至其他影像模态与解剖结构。
原文摘要 · Abstract (English)
Mesh reconstruction is a cornerstone process across various applications, including in-silico trials, digital twins, surgical planning, and navigation. Recent advancements in deep learning have notably enhanced mesh reconstruction speeds. Yet, traditional methods predominantly rely on deforming a standardised template mesh for individual subjects, which overlooks the unique anatomical variations between them, and may compromise the fidelity of the reconstructions. In this paper, we propose an adaptive-template-based mesh reconstruction network (ATMRN), which generates adaptive templates from the given images for the subsequent deformation, moving beyond the constraints of a singular, fixed template. Our approach, validated on cortical magnetic resonance (MR) images from the OASIS dataset, sets a new benchmark in voxel-to-cortex mesh reconstruction, achieving an average symmetric surface distance of 0.267mm across four cortical structures. Our proposed method is generic and can be easily transferred to other image modalities and anatomical structures.
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