PRISM用神经网络建模形状演化，同时给出空间可变的不确定性估计。
PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
- 将隐式神经表示与统计形状分析结合，建模形状条件分布
- 实现任意位置的均值与协变量相关不确定性的连续估计
- 提供可解释的临床级不确定性，适合医学影像分析
理解解剖形状如何随发育协变量变化，并量化其空间异质性不确定性，在医疗研究中至关重要。现有方法多依赖全局时间扭曲模型，忽略空间动态差异。我们提出PRISM，一种将隐式神经表示与不确定性感知统计形状分析结合的新框架。PRISM建模给定协变量下形状的条件分布，可在任意位置提供群体均值及协变量相关不确定性的连续估计。关键理论贡献是闭式费舍尔信息度量，通过自动微分实现高效、解析可计算的局部时序不确定性量化。在三个合成数据集和一个临床数据集上的实验表明，PRISM在统一框架内展现出强大性能，涵盖形状演化建模、个性化形状预测与异常检测等任务，同时提供可解释且临床有意义的不确定性估计。
原文摘要 · Abstract (English)
Understanding how anatomical shapes evolve in response to developmental covariates - and quantifying their spatially varying uncertainties - is critical in healthcare research. Existing approaches typically rely on global time-warping formulations that ignore spatially heterogeneous dynamics. We introduce PRISM, a novel framework that bridges implicit neural representations with uncertainty-aware statistical shape analysis. PRISM models the conditional distribution of shapes given covariates, providing spatially continuous estimates of both the population mean and covariate-dependent uncertainty at arbitrary locations. A key theoretical contribution is a closed-form Fisher Information metric that enables efficient, analytically tractable local temporal uncertainty quantification via automatic differentiation. Experiments on three synthetic datasets and one clinical dataset demonstrate PRISM's strong performance across diverse tasks - from modeling shape evolution to personalized shape prediction and anomaly detection - within a unified framework, while providing interpretable and clinically meaningful uncertainty estimates.
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