Managing class imbalance and differential staining of immune cell populations in multi-class instance segmentation of multiplexed immunofluorescence images of Lupus Nephritis biopsies

Lupus nephritis (LuN) is a manifestation of systemic lupus erythematosus defined by chronic infiltration of immune cells into the kidneys—particularly lymphocytes and dendritic cells (DCs). Ultimately, our goal is to characterize the cellular communities associated with progression to kidney failure. To accomplish this, we have generated a dataset of fluorescence confocal microscopy images of kidney biopsies from 31 LuN patients that have been stained for two T-lymphocyte populations, B-lymphocytes and two DC populations. We are using convolutional neural networks (CNNs) with a Mask R-CNN architecture to perform instance segmentation on these five classes. This multi-class instance segmentation task is hindered by an inherent class imbalance between lymphocytes and DCs, with DCs being much less prevalent. Here we discuss methods for managing class imbalance to achieve comparable instance segmentation of both DCs and lymphocytes in LuN biopsies. A network trained to identify all 5 classes yielded higher sensitivity to DCs when the training set was filtered to contain images with all 5 cell classes present. Average DC sensitivity on an independent test set improved from 0.54 to 0.63 with filtered training data. DC segmentation improved further when the network was trained specifically for DC classes. Average DC sensitivity reached 0.91 when trained separately from lymphocytes, with average Jaccard index of DCs improving from 0.69±0.2 to 0.76±0.2. Accurate segmentation of all cell types relevant to LuN pathogenesis enabled in-depth spatial analysis of the immune environments that result in renal failure in LuN patients.