  There has been a long pursuit for precise and reproducible glomerular
quantification on renal pathology to leverage both research and practice. When
digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of
serial sections from the same tissue can be acquired as a stack of images,
similar to frames in a video. In radiology, the stack of images (e.g., computed
tomography) are naturally used to provide 3D context for organs, tissues, and
tumors. In pathology, it is appealing to do a similar 3D assessment. However,
the 3D identification and association of large-scale glomeruli on renal
pathology is challenging due to large tissue deformation, missing tissues, and
artifacts from WSI. In this paper, we propose a novel Multi-object Association
for Pathology in 3D (Map3D) method for automatically identifying and
associating large-scale cross-sections of 3D objects from routine serial
sectioning and WSI. The innovations of the Map3D method are three-fold: (1) the
large-scale glomerular association is formed as a new multi-object tracking
(MOT) perspective; (2) the quality-aware whole series registration is proposed
to not only provide affinity estimation but also offer automatic kidney-wise
quality assurance (QA) for registration; (3) a dual-path association method is
proposed to tackle the large deformation, missing tissues, and artifacts during
tracking. To the best of our knowledge, the Map3D method is the first approach
that enables automatic and large-scale glomerular association across 3D serial
sectioning using WSI. Our proposed method Map3D achieved MOTA= 44.6, which is
12.1% higher than the non deep learning benchmarks.
