Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg

TL;DR

This paper introduces FH-Seg, a hierarchical learning framework with full-scale skip connections and attention gates for precise renal vasculature segmentation, supported by a new large annotated dataset, achieving state-of-the-art accuracy.

Contribution

The paper presents a novel FH-Seg framework with full-scale hierarchical learning and a new renal vasculature dataset, advancing fine-grained segmentation accuracy.

Findings

FH-Seg achieves 71.23% Dice score, outperforming previous methods.

The LRV dataset contains 16,212 annotated images of renal arteries.

FH-Seg demonstrates superior segmentation performance on the LRV dataset.

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.