用AI补全切片，让脑解剖照片重建更精细真实
Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation
- 通过AI超分辨率补全切片，生成各向同性高精度3D结构
- 在皮层和白质区域分割Dice分数显著提升，最高达0.92
- 适用于厚切片数据，适合神经病理与影像融合研究
神经病理分析依赖空间精确的三维体积重建，以增强解剖边界识别并提高形态测量准确性。此前工作已证明可从二维解剖照片重建3D脑结构，但重建结果常呈现粗糙、过度平滑的问题，尤其在高各向异性（如厚切片）情况下更为明显。本文提出一种计算高效的超分辨率补全步骤，通过合成数据域随机化训练，将各向异性重建转换为解剖一致的各向同性体积。该方法在不同解剖协议下均表现稳健，对大厚度切片仍具鲁棒性。改进后的重建在自动分割任务中取得更高Dice评分，皮层与白质区域尤为显著。表面重建与图谱配准验证显示，皮层表面更准确，与MRI配准效果更优。本方法显著提升了基于照片重建的分辨率与解剖保真度，强化了神经病理学与神经影像学之间的联系。代码公开于https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon。
原文摘要 · Abstract (English)
Neuropathological analyses benefit from spatially precise volumetric reconstructions that enhance anatomical delineation and improve morphometric accuracy. Our prior work has shown the feasibility of reconstructing 3D brain volumes from 2D dissection photographs. However these outputs sometimes exhibit coarse, overly smooth reconstructions of structures, especially under high anisotropy (i.e., reconstructions from thick slabs). Here, we introduce a computationally efficient super-resolution step that imputes slices to generate anatomically consistent isotropic volumes from anisotropic 3D reconstructions of dissection photographs. By training on domain-randomized synthetic data, we ensure that our method generalizes across dissection protocols and remains robust to large slab thicknesses. The imputed volumes yield improved automated segmentations, achieving higher Dice scores, particularly in cortical and white matter regions. Validation on surface reconstruction and atlas registration tasks demonstrates more accurate cortical surfaces and MRI registration. By enhancing the resolution and anatomical fidelity of photograph-based reconstructions, our approach strengthens the bridge between neuropathology and neuroimaging. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。