Abstract
There has been a long pursuit for precise and reproducible glomerular quantification in the field of renal pathology in both research and clinical practice. Currently, 3D glomerular identification and reconstruction of large-scale glomeruli are labor-intensive tasks, and time-consuming by manual analysis on whole slide imaging (WSI) in 2D serial sectioning representation. The accuracy of serial section analysis is also limited in the 2D serial context. Moreover, there are no approaches to present 3D glomerular visualization for human examination (volume calculation, 3D phenotype analysis, etc.). In this paper, we introduce an end-to-end holistic deep-learning-based method that achieves automatic detection, segmentation and multi-object tracking (MOT) of individual glomeruli with large-scale glomerular-registered assessment in a 3D context on WSIs. The high-resolution WSIs are the inputs, while the outputs are the 3D glomerular reconstruction and volume estimation. This pipeline achieves 81.8 in IDF1 and 69.1 in MOTA as MOT performance, while the proposed volume estimation achieves 0.84 Spearman correlation coefficient with manual annotation. The end-to-end MAP3D+ pipeline provides an approach for extensive 3D glomerular reconstruction and volume quantification from 2D serial section WSIs.