用稀疏结构压缩血管网络，实现高精度重建与生成
Sparse Representation Learning for Vessels
- 基于稀疏卷积与注意力机制，实现8×8×8空间压缩
- 在亚毫米级分辨率下完成全器官血管网络重建
- 生成的潜空间可直接用于病变分类与真实血管合成
分析人体血管及气道等管状结构对疾病诊断和治疗至关重要。现有方法多依赖小区域或简化树状结构，难以在临床分辨率下处理全器官级网络。为此，我们提出VAEsselSparse——一种高效的编码器-解码器模型，可在亚毫米分辨率下获得全器官级血管网络的有意义且紧凑的表示。该模型通过稀疏卷积与注意力机制利用3D血管结构的固有稀疏性，实现8×8×8的空间压缩率。相比密集型模型与先前方法，其重建性能更优；且所得潜空间保留了可用于分类任务（如动脉瘤/狭窄、Willis环亚型）的临床相关判别特征。此外，该紧凑潜空间还可作为生成模型学习血管特异性先验的基础，实现真实血管的合成。
原文摘要 · Abstract (English)
Analyzing human vasculature and vessel-like, tubular structures, such as airways, is crucial for disease diagnosis and treatment. Current methods often rely on small sub-regions or simplified tree-like structures, rendering analysis of entire organ-level networks at clinical resolution computationally challenging. To this end, we propose VAEsselSparse, an efficient encoder-decoder model to obtain a meaningful yet compact representation of the entire organ-level vascular network at sub-millimeter resolution. VAEsselSparse leverages the inherent sparsity of 3D vascular structures via sparse convolutions and attention mechanisms, achieving substantial spatial compression rates of 8 x 8 x 8. We demonstrate superior reconstruction performance compared to dense counterparts and previous methods. Importantly, the resulting latent space retains clinically relevant discriminative features readily usable for classification tasks, such as aneurysm/stenosis or subvariants of the circle of Willis. Moreover, the compact latent space of VAEsselSparse serves as an effective representation for learning vessel-specific priors through generative models, enabling the synthesis of realistic vasculature.
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