标题： 冠状脑组织切片的自动化分割用于三维神经病理学 显示英文标题

标题： Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology

摘要： 近年来，图像配准和机器学习的进展使得从常规矢状切片照片中对\emph{死后检查}脑组织进行体积分析成为可能，这些照片在世界各地的脑库和神经病理学实验室中经常被收集。 这种方法的一个缺点是需要从照片中分割组织，这目前需要昂贵的人工干预。 在本文中，我们提出了一种深度学习模型来自动化这一过程。 自动分割工具依赖于一个U-Net架构，该架构使用了以下组合进行训练： \textit{(i)}1,414张手动分割的固定和新鲜组织照片，来自不同诊断的标本，在两个不同的地点拍摄；以及 \textit{(ii)}约2,000张合成图像，具有随机对比度和相应的掩码，从MRI扫描生成，以提高对未见过的摄影设置的泛化能力。 对训练中未见过的照片子集的自动模型预测进行了分析，以估计与手动标签相比的性能——包括交叉和内部评分者变异性。 我们的模型达到了中位数Dice分数超过0.98，平均表面距离低于0.4毫米，95%的Hausdorff距离低于1.60毫米，这接近交叉和内部评分者水平。 我们的工具可在surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools公开获取。 显示英文摘要

摘要： Advances in image registration and machine learning have recently enabled volumetric analysis of \emph{postmortem} brain tissue from conventional photographs of coronal slabs, which are routinely collected in brain banks and neuropathology laboratories worldwide. One caveat of this methodology is the requirement of segmentation of the tissue from photographs, which currently requires costly manual intervention. In this article, we present a deep learning model to automate this process. The automatic segmentation tool relies on a U-Net architecture that was trained with a combination of \textit{(i)}1,414 manually segmented images of both fixed and fresh tissue, from specimens with varying diagnoses, photographed at two different sites; and \textit{(ii)}~2,000 synthetic images with randomized contrast and corresponding masks generated from MRI scans for improved generalizability to unseen photographic setups. Automated model predictions on a subset of photographs not seen in training were analyzed to estimate performance compared to manual labels -- including both inter- and intra-rater variability. Our model achieved a median Dice score over 0.98, mean surface distance under 0.4~mm, and 95\% Hausdorff distance under 1.60~mm, which approaches inter-/intra-rater levels. Our tool is publicly available at surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools.