MorphoFlow：基于自适应潜在相关性的稀疏监督生成形状建模
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance
AI总结：
MorphoFlow通过自适应潜在相关性权重，从稀疏表面标注直接学习紧凑的概率形状表示，实现高分辨率重建和解构解剖变异模式。
摘要：
统计形状建模（SSM）在群体层面分析解剖变异中至关重要，但现有方法依赖密集标注分割和固定潜在表示，限制了扩展性和灵活性。我们引入MorphoFlow，一种稀疏监督生成形状建模框架，直接从稀疏表面标注学习紧凑的概率形状表示。MorphoFlow整合了神经隐式形状表示与自解码器形式和自回归归一化流，学习潜在形状空间上的表达性概率密度。神经隐式表示支持分辨率无关的3D解剖建模，自解码器形式支持在稀疏监督下直接优化实例潜在代码。自回归流捕捉潜在解剖变异分布，提供可 tractable 的基于似然的生成模型。通过稀疏诱导先验，我们引入自适应潜在相关性权重，使模型能根据潜在解剖变异的相关性调节单个潜在维度的贡献，同时保持生成表达性。所得潜在空间支持不确定性量化和解剖上合理的形状合成，无需手动调整潜在维度。在公开可用的腰椎和股骨数据集上评估显示，从稀疏输入中实现了高分辨率重建，并恢复了与群体水平趋势一致的结构化解剖变异模式。
英文摘要：
Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed latent representations. These requirements limit scalability and reduce flexibility when modeling complex anatomical variation. We introduce MorphoFlow, a sparse supervised generative shape modeling framework that learns compact probabilistic shape representations directly from sparse surface annotations. MorphoFlow integrates neural implicit shape representations with an autodecoder formulation and autoregressive normalizing flows to learn an expressive probabilistic density over the latent shape space. The neural implicit representation enables resolution-agnostic modeling of 3D anatomy, while the autodecoder formulation supports direct optimization of per-instance latent codes under sparse supervision. The autoregressive flow captures the distribution of latent anatomical variability providing a tractable, likelihood-based generative model of shapes. To promote compact and structured latent representations, we incorporate adaptive latent relevance weighting through sparsity-inducing priors, enabling the model to regulate the contribution of individual latent dimensions according to their relevance to the underlying anatomical variation while preserving generative expressivity. The resulting latent space supports uncertainty quantification and anatomically plausible shape synthesis without manual latent dimensionality tuning. Evaluation on publicly available lumbar vertebrae and femur datasets demonstrates accurate high-resolution reconstruction from sparse inputs and recovery of structured modes of anatomical variation consistent with population level trends.
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