  Efficiently quantifying renal structures can provide distinct spatial context
and facilitate biomarker discovery for kidney morphology. However, the
development and evaluation of the transformer model to segment the renal
cortex, medulla, and collecting system remains challenging due to data
inefficiency. Inspired by the hierarchical structures in vision transformer, we
propose a novel method using a 3D block aggregation transformer for segmenting
kidney components on contrast-enhanced CT scans. We construct the first cohort
of renal substructures segmentation dataset with 116 subjects under
institutional review board (IRB) approval. Our method yields the
state-of-the-art performance (Dice of 0.8467) against the baseline approach of
0.8308 with the data-efficient design. The Pearson R achieves 0.9891 between
the proposed method and manual standards and indicates the strong correlation
and reproducibility for volumetric analysis. We extend the proposed method to
the public KiTS dataset, the method leads to improved accuracy compared to
transformer-based approaches. We show that the 3D block aggregation transformer
can achieve local communication between sequence representations without
modifying self-attention, and it can serve as an accurate and efficient
quantification tool for characterizing renal structures.
