Transformer-based multiclass segmentation pipeline for basic kidney histology

Multiclass segmentation of microanatomy in kidney biopsies is an important and non-trivial task in computational renal pathology. In a multicenter study, we densely annotated basic anatomical objects (glomeruli, tubules, and vessels) in 261 regions of interest of 147 kidney biopsy WSIs sourced from the archives of hospitals in Amsterdam, Utrecht, and Leiden (Netherlands). And we trained multiple UNet- and Mask2Former-based models on WSI-level and patch-level splitting methods, and compared their performance across training strategies. Test performance was assessed on 24 annotated renal WSIs from Leuven (Belgium) with sensitivity analysis on the extent of fibrosis and inflammation. At the patch-level, UNet-ResNet18 achieved comparable performances to M2F-Swin-B with average Intersection over Union of all classes (A-IoU, 0.84 vs 0.94), as well as per-class IoU. However, at the WSI-level, M2F-Swin-B significantly surpassed UNet-ResNet18 with large margins on A-IoU (0.84 vs 0.48), with similar observed in per-class IoU. Notably, M2F-Swin-B outperformed UNet-ResNet18 in scenarios characterized by a higher degree of fibrosis and inflammation (A-IoU, 0.76 vs 0.66). Furthermore, at the WSI-level, M2F-Swin-B achieved IoU score of arteries to 0.58, whereas UNet-ResNet18 only achieved 0.33. In this study, we found that the attention mechanism in Mask2Former enables visibly crisper and more uniform segmentation, particularly when data is inadequate. Mask2Former-based models outperform UNet-based models in challenging areas from inflamed and fibrotic renal biopsies.