EMGauss：通过动态高斯建模在体积电子显微镜中实现连续切片到三维重建
EMGauss: Continuous Slice-to-3D Reconstruction via Dynamic Gaussian Modeling in Volume Electron Microscopy
AI总结：
EMGauss通过动态高斯建模方法，解决体积电子显微镜中切片到三维重建的异质性限制，提升插值质量并实现连续切片合成。
摘要：
体积电子显微镜（vEM）能够实现生物结构的纳米级三维成像，但受限于采集权衡，导致各向异性体积和有限的轴向分辨率。现有深度学习方法通过利用横向先验来恢复各向同性，但其假设在形态各向异性结构中失效。我们提出EMGauss，一种从平面扫描的2D切片进行3D重建的一般框架，适用于vEM。我们的关键创新是将切片到3D重建重新表述为基于高斯散射的3D动态场景渲染问题，其中轴向切片的进展被建模为2D高斯点云的时序演化。为了增强数据稀疏区域的保真度，我们引入了教师-学生 bootstrap 机制，利用高置信度预测的未观测切片作为伪监督信号。与扩散和GAN基的重建方法相比，EMGauss显著提高了插值质量，实现了连续切片合成，并消除了大规模预训练的需要。除了vEM外，它还可能在不同成像领域中提供通用的切片到3D解决方案。
英文摘要：
Volume electron microscopy (vEM) enables nanoscale 3D imaging of biological structures but remains constrained by acquisition trade-offs, leading to anisotropic volumes with limited axial resolution. Existing deep learning methods seek to restore isotropy by leveraging lateral priors, yet their assumptions break down for morphologically anisotropic structures. We present EMGauss, a general framework for 3D reconstruction from planar scanned 2D slices with applications in vEM, which circumvents the inherent limitations of isotropy-based approaches. Our key innovation is to reframe slice-to-3D reconstruction as a 3D dynamic scene rendering problem based on Gaussian splatting, where the progression of axial slices is modeled as the temporal evolution of 2D Gaussian point clouds. To enhance fidelity in data-sparse regimes, we incorporate a Teacher-Student bootstrapping mechanism that uses high-confidence predictions on unobserved slices as pseudo-supervisory signals. Compared with diffusion- and GAN-based reconstruction methods, EMGauss substantially improves interpolation quality, enables continuous slice synthesis, and eliminates the need for large-scale pretraining. Beyond vEM, it potentially provides a generalizable slice-to-3D solution across diverse imaging domains.
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