用深度学习自动分割脑组织切片图像，精度接近人工水平。
Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology
- 基于U-Net架构，训练数据来自1414张手动标注的固定与新鲜组织图像。
- 平均Dice系数超0.98，表面距离均值低于0.4mm，逼近人工标注一致性。
- 适用于神经病理学研究者，可加速脑组织三维分析流程。
影像配准与机器学习的进步使得从常规冠状切片照片中实现尸检脑组织的三维分析成为可能，这类照片在世界各地的脑库和神经病理实验室中广泛收集。然而，该方法的一个局限是需对组织进行分割，目前仍依赖昂贵的人工操作。本文提出一种深度学习模型以自动化该过程。该模型采用U-Net架构，使用来自不同诊断类型、在两个不同地点拍摄的1,414张固定与新鲜组织的照片进行训练。在未参与训练的子集照片上评估模型性能，结果与人工标注对比，包括人与人之间及同一个人多次标注间的变异性。模型在中位数Dice分数超过0.98，平均表面距离低于0.4毫米，95%豪斯多夫距离低于1.60毫米，接近人与人之间的变异水平。该工具已公开发布于surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools。
原文摘要 · Abstract (English)
Advances in image registration and machine learning have recently enabled volumetric analysis of 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 1,414 manually segmented images of both fixed and fresh tissue, from specimens with varying diagnoses, photographed at two different sites. 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 0.4mm, and 95\% Hausdorff distance under 1.60mm, which approaches inter-/intra-rater levels. Our tool is publicly available at surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools.
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