Abstract
Coronary calcification creates blooming artifacts in Computed Tomography Angiography (CTA), severely hampering the diagnosis of lumen stenosis. While Deep Convolutional Neural Networks (DCNNs) like Dense-Unet have shown promise in removing these artifacts via inpainting, they often require large labeled datasets which are scarce in the medical domain. Inspired by recent advancements in Masked Autoencoders (MAE) for 3D point clouds, we propose \textbf{Dense-MAE}, a novel self-supervised learning framework for volumetric medical data. We introduce a pre-training strategy that randomly masks 3D patches of the vessel lumen and trains the Dense-Unet to reconstruct the missing geometry. This forces the encoder to learn high-level latent features of arterial topology without human annotation. Experimental results on clinical CTA datasets demonstrate that initializing the Calcium Removal network with our MAE-based weights significantly improves inpainting accuracy and stenosis estimation compared to training from scratch, specifically in few-shot scenarios.
Abstract (translated)
冠状钙化在计算机断层血管成像（CTA）中会产生“开花”伪影，严重阻碍了管腔狭窄的诊断。尽管深度卷积神经网络（DCNN），如Dense-Unet，在通过图像修复去除这些伪影方面显示出了一定潜力，但它们通常需要大量标注的数据集，而这类数据在医学领域往往较为稀缺。受最近在3D点云中应用遮罩自编码器（MAE）进展的启发，我们提出了一种新的针对体积型医疗数据的自监督学习框架——**Dense-MAE**。我们的方法引入了预训练策略：随机屏蔽血管管腔中的3D补丁，并通过训练Dense-Unet来重构缺失的几何结构。这迫使编码器在没有人工标注的情况下，学习动脉拓扑的高级潜在特征。 实验结果表明，在临床CTA数据集上使用基于我们MAE权重初始化钙去除网络的方法，在几例样本（few-shot）场景下，显著提高了修复精度和狭窄程度估计的准确性，相比从头开始训练有明显改进。
URL
https://arxiv.org/abs/2601.02392
