Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. Detailed studies of the microglial response after TBI require high throughput quantification of changes in microglial count and morphology in histological sections throughout the brain. In this paper, we present a fully automated end-to-end system that is capable of assessing microglial activation in white matter regions on whole slide images of Iba1 stained sections. Our approach involves the division of the full brain slides into smaller image patches that are subsequently automatically classified into white and grey matter sections. On the patches classified as white matter, we jointly apply functional minimization methods and deep learning classification to identify Iba1-immunopositive microglia. Detected cells are then automatically traced to preserve their complex branching structure after which fractal analysis is applied to determine the activation states of the cells. The resulting system detects white matter regions with 84% accuracy, detects microglia with a performance level of 0.70 (F1 score, the harmonic mean of precision and sensitivity) and performs binary microglia morphology classification with a 70% accuracy. This automated pipeline performs these analyses at a 20-fold increase in speed when compared to a human pathologist. Moreover, we have demonstrated robustness to variations in stain intensity common for Iba1 immunostaining. A preliminary analysis was conducted that indicated that this pipeline can identify differences in microglia response due to TBI. An automated solution to microglia cell analysis can greatly increase standardized analysis of brain slides, allowing pathologists and neuroscientists to focus on characterizing the associated underlying diseases and injuries.

中文翻译：

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在整个玻片成像中自动表征Iba1免疫阳性小胶质细胞的端到端系统。

颅脑外伤（TBI）是全球范围内死亡和残疾的主要原因之一。TBI后小胶质细胞反应的详细研究要求高通量量化整个大脑组织学切片中小胶质细胞计数和形态的变化。在本文中，我们提出了一个全自动的端到端系统，该系统能够评估Iba1染色切片的整个幻灯片图像上白质区域中的小胶质细胞活化。我们的方法涉及将完整的大脑滑片划分为较小的图像块，然后将其自动分类为白和灰质部分。在分类为白质的斑块上，我们联合应用功能最小化方法和深度学习分类来识别Iba1免疫阳性小胶质细胞。然后自动跟踪检测到的细胞以保留其复杂的分支结构，然后进行分形分析以确定细胞的活化状态。最终的系统以84％的准确度检测白质区域，以0.70的性能水平检测小胶质细胞（F1评分，精确度和灵敏度的谐波平均值），并以70％的准确度执行二值小胶质细胞形态分类。与人类病理学家相比，该自动化管道以20倍的速度执行这些分析。此外，我们已经证明了对于Iba1免疫染色常见的染色强度变化的鲁棒性。进行的初步分析表明，该管线可以识别由于TBI引起的小胶质细胞反应的差异。