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.
Keywords: deep learning; high-throughput image analysis; immunology; instance segmentation.