Significance: Lupus nephritis (LuN) is a chronic inflammatory kidney disease. The cellular mechanisms by which LuN progresses to kidney failure are poorly characterized. Automated instance segmentation of immune cells in immunofluorescence images of LuN can probe these cellular interactions. Aim: Our specific goal is to quantify how sample fixation and staining panel design impact automated instance segmentation and characterization of immune cells. Approach: Convolutional neural networks (CNNs) were trained to segment immune cells in fluorescence confocal images of LuN biopsies. Three datasets were used to probe the effects of fixation methods on cell features and the effects of one-marker versus two-marker per cell staining panels on CNN performance. Results: Networks trained for multi-class instance segmentation on fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) samples stained with a two-marker panel had sensitivities of 0.87 and 0.91 and specificities of 0.82 and 0.88, respectively. Training on samples with a one-marker panel reduced sensitivity (0.72). Cell size and intercellular distances were significantly smaller in FFPE samples compared to fresh frozen (Kolmogorov–Smirnov, p ≪ 0.0001). Conclusions: Fixation method significantly reduces cell size and intercellular distances in LuN biopsies. The use of two markers to identify cell subsets showed improved CNN sensitivity relative to using a single marker.

中文翻译：

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量化活检固定和染色面板设计对人狼疮性肾炎免疫细胞自动实例分割的影响

意义：狼疮性肾炎（LuN）是一种慢性炎症性肾脏疾病。LuN 进展为肾衰竭的细胞机制尚不清楚。LuN 免疫荧光图像中免疫细胞的自动实例分割可以探测这些细胞相互作用。目标：我们的具体目标是量化样本固定和染色面板设计如何影响免疫细胞的自动实例分割和表征。方法：训练卷积神经网络 (CNN) 以在 LuN 活检的荧光共焦图像中分割免疫细胞。三个数据集用于探测固定方法对细胞特征的影响，以及每个细胞染色面板一个标记与两个标记对 CNN 性能的影响。结果：对新鲜冷冻和福尔马林固定石蜡包埋 (FFPE) 样本进行多类实例分割训练的网络用双标记面板染色，其灵敏度分别为 0.87 和 0.91，特异性分别为 0.82 和 0.88。使用单标记面板训练样本降低了灵敏度 (0.72)。与新鲜冷冻的相比，FFPE 样品中的细胞大小和细胞间距离明显更小（Kolmogorov-Smirnov，p ≪ 0.0001）。结论：固定方法显着降低了 LuN 活检中的细胞大小和细胞间距离。相对于使用单个标记，使用两个标记来识别细胞子集显示了改进的 CNN 敏感性。使用单标记面板训练样本降低了灵敏度 (0.72)。与新鲜冷冻的相比，FFPE 样品中的细胞大小和细胞间距离明显更小（Kolmogorov–Smirnov，p ≪ 0.0001）。结论：固定方法显着降低了 LuN 活检中的细胞大小和细胞间距离。相对于使用单个标记，使用两个标记来识别细胞子集显示了改进的 CNN 敏感性。使用单标记面板训练样本降低了灵敏度 (0.72)。与新鲜冷冻的相比，FFPE 样品中的细胞大小和细胞间距离明显更小（Kolmogorov-Smirnov，p ≪ 0.0001）。结论：固定方法显着降低了 LuN 活检中的细胞大小和细胞间距离。相对于使用单个标记，使用两个标记来识别细胞子集显示了改进的 CNN 敏感性。