Abstract：
OBJECTIVE: Deep learning has firmly established its dominance in medical imaging applications. However, careful consideration must be exercised when transitioning a trained source model to adapt to an entirely distinct environment that deviates significantly from the training set. The majority of the efforts to mitigate this issue have predominantly focused on classification and segmentation tasks. In this work, we perform a domain adaptation of a trained source model to reconstruct high-resolution intervertebral disc meshes from low-resolution MRI.
METHODS: To address the outlined challenges, we use MRI2Mesh as the shape reconstruction network. It incorporates three major modules: image encoder, mesh deformation, and cross-level feature fusion. This feature fusion module is used to encapsulate local and global disc features. We evaluate two major domain adaptation techniques: adaptive batch normalization (AdaBN) and adaptive instance normalization (AdaIN) for the task of shape reconstruction.
RESULTS: Experiments conducted on distinct datasets, including data from different populations, machines, and test sites demonstrate the effectiveness of MRI2Mesh for domain adaptation. MRI2Mesh achieved up to a 14% decrease in Hausdorff distance (HD) and a 19% decrease in the point-to-surface (P2S) metric for both AdaBN and AdaIN experiments, indicating improved performance.
CONCLUSIONS: MRI2Mesh has demonstrated consistent superiority to the state-of-the-art Voxel2Mesh network across a diverse range of datasets, populations, and scanning protocols, highlighting its versatility. Additionally, AdaBN has emerged as a robust method compared to the AdaIN technique. Further experiments show that MRI2Mesh, when combined with AdaBN, holds immense promise for enhancing the precision of anatomical shape reconstruction in domain adaptation.
METHODS: To address the outlined challenges, we use MRI2Mesh as the shape reconstruction network. It incorporates three major modules: image encoder, mesh deformation, and cross-level feature fusion. This feature fusion module is used to encapsulate local and global disc features. We evaluate two major domain adaptation techniques: adaptive batch normalization (AdaBN) and adaptive instance normalization (AdaIN) for the task of shape reconstruction.
RESULTS: Experiments conducted on distinct datasets, including data from different populations, machines, and test sites demonstrate the effectiveness of MRI2Mesh for domain adaptation. MRI2Mesh achieved up to a 14% decrease in Hausdorff distance (HD) and a 19% decrease in the point-to-surface (P2S) metric for both AdaBN and AdaIN experiments, indicating improved performance.
CONCLUSIONS: MRI2Mesh has demonstrated consistent superiority to the state-of-the-art Voxel2Mesh network across a diverse range of datasets, populations, and scanning protocols, highlighting its versatility. Additionally, AdaBN has emerged as a robust method compared to the AdaIN technique. Further experiments show that MRI2Mesh, when combined with AdaBN, holds immense promise for enhancing the precision of anatomical shape reconstruction in domain adaptation.
摘要：
目的：深度学习已在医学成像应用中确立了主导地位。然而,在转换训练的源模型以适应与训练集明显不同的完全不同的环境时，必须仔细考虑。缓解这一问题的大多数努力主要集中在分类和细分任务上。在这项工作中,我们对经训练的源模型进行域自适应,以从低分辨率MRI重建高分辨率椎间盘网格.
方法：为了应对上述挑战，我们使用MRI2Mesh作为形状重建网络。它包含三个主要模块：图像编码器，网格变形,和跨级别特征融合。该特征融合模块用于封装局部和全局盘特征。我们评估了两种主要的领域适应技术：用于形状重建任务的自适应批量归一化（AdaBN）和自适应实例归一化（AdaIN）。
结果：在不同的数据集上进行的实验，包括来自不同人群的数据，机器,和测试站点证明了MRI2Mesh用于域适应的有效性。对于AdaBN和AdaIN实验，MRI2Mesh的Hausdorff距离（HD）降低了14％，点到面（P2S）度量降低了19％。表明改进的性能。
结论：MRI2Mesh在各种数据集上显示出与最先进的Voxel2Mesh网络的一致优势，人口,和扫描协议，突出了它的多功能性。此外,与AdaIN技术相比，AdaBN已成为一种强大的方法。进一步的实验表明，MRI2Mesh，当与AdaBN结合时，具有巨大的希望，以提高在域适应解剖形状重建的精度。
方法：为了应对上述挑战，我们使用MRI2Mesh作为形状重建网络。它包含三个主要模块：图像编码器，网格变形,和跨级别特征融合。该特征融合模块用于封装局部和全局盘特征。我们评估了两种主要的领域适应技术：用于形状重建任务的自适应批量归一化（AdaBN）和自适应实例归一化（AdaIN）。
结果：在不同的数据集上进行的实验，包括来自不同人群的数据，机器,和测试站点证明了MRI2Mesh用于域适应的有效性。对于AdaBN和AdaIN实验，MRI2Mesh的Hausdorff距离（HD）降低了14％，点到面（P2S）度量降低了19％。表明改进的性能。
结论：MRI2Mesh在各种数据集上显示出与最先进的Voxel2Mesh网络的一致优势，人口,和扫描协议，突出了它的多功能性。此外,与AdaIN技术相比，AdaBN已成为一种强大的方法。进一步的实验表明，MRI2Mesh，当与AdaBN结合时，具有巨大的希望，以提高在域适应解剖形状重建的精度。
