提出分层学习框架FH-Seg，精准分割肾脏血管细节。
Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg
- 构建全尺度跳跃连接融合结构与语义信息
- 用可学习注意力门提升关键血管特征识别率
- 新数据集含1.6万张标注图像，适合病理分析研究
精确的肾脏血管细粒度分割对肾病分析至关重要，但受限于多样且标注不足的图像。现有方法难以准确分割血管内壁、外壁、动脉及病变等复杂区域。本文提出FH-Seg，一种全尺度分层学习框架，通过全尺度跳跃连接融合多尺度解剖细节与上下文语义，有效弥合结构与病理语境间的差距；同时引入可学习的分层软注意力门，自适应抑制非核心信息干扰，强化关键血管特征关注。为推动肾脏病理分割研究，我们构建了大型肾脏血管数据集LRV，包含5,600个肾动脉的16,212张细粒度标注图像。在LRV数据集上的大量实验表明，FH-Seg取得71.23%的Dice分数和73.06%的F1分数，优于Omni-Seg 2.67和2.13个百分点。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。