Reconsider the Template Mesh in Deep Learning-based Mesh Reconstruction
This work addresses the limitation of fixed templates in mesh reconstruction for medical applications like surgical planning, offering a more accurate method for capturing anatomical variations.
The paper tackles the problem of mesh reconstruction in medical imaging by proposing an adaptive-template-based network that generates subject-specific templates instead of using a fixed one, achieving an average symmetric surface distance of 0.267mm on cortical MR images from the OASIS dataset.
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.