Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg
Journal:
arXiv
Published Date:
Feb 7, 2025
Abstract
Accurate fine-grained segmentation of the renal vasculature is critical for
nephrological analysis, yet it faces challenges due to diverse and
insufficiently annotated images. Existing methods struggle to accurately
segment intricate regions of the renal vasculature, such as the inner and outer
walls, arteries and lesions. In this paper, we introduce FH-Seg, a Full-scale
Hierarchical Learning Framework designed for comprehensive segmentation of the
renal vasculature. Specifically, FH-Seg employs full-scale skip connections
that merge detailed anatomical information with contextual semantics across
scales, effectively bridging the gap between structural and pathological
contexts. Additionally, we implement a learnable hierarchical soft attention
gates to adaptively reduce interference from non-core information, enhancing
the focus on critical vascular features. To advance research on renal pathology
segmentation, we also developed a Large Renal Vasculature (LRV) dataset, which
contains 16,212 fine-grained annotated images of 5,600 renal arteries.
Extensive experiments on the LRV dataset demonstrate FH-Seg's superior
accuracies (71.23% Dice, 73.06% F1), outperforming Omni-Seg by 2.67 and 2.13
percentage points respectively. Code is available at:
https://github.com/hrlblab/FH-seg.
