The study of physiology demonstrates that the form (shape)of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical research. Statistical shape modeling (SSM) is a widely used tool for quantitative analysis of forms of anatomies, aiding in characterizing and identifying differences within a population of subjects. Despite its utility, the conventional SSM construction pipeline is often complex and time-consuming. Additionally, reliance on linearity assumptions further limits the model from capturing clinically relevant variations. Recent advancements in deep learning solutions enable the direct inference of SSM from unsegmented medical images, streamlining the process and improving accessibility. However, the new methods of SSM from images do not adequately account for situations where the imaging data quality is poor or where only sparse information is available. Moreover, quantifying aleatoric uncertainty, which represents inherent data variability, is crucial in deploying deep learning for clinical tasks to ensure reliable model predictions and robust decision-making, especially in challenging imaging conditions. Therefore, we propose SPI-CorrNet, a unified model that predicts 3D correspondences from sparse imaging data. It leverages a teacher network to regularize feature learning and quantifies data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variances. Experiments on the LGE MRI left atrium dataset and Abdomen CT-1K liver datasets demonstrate that our technique enhances the accuracy and robustness of sparse image-driven SSM.

通过分析解剖结构的形态来研究生理学，统计形态建模是一种用于定量解析解剖结构形态差异的工具。然而，传统的形态建模方法复杂耗时，线性假设限制了模型的应用范围。最新的深度学习技术能够直接从未分割的医学影像中推断统计形态模型，简化了流程并提升了可用性。然而，现有的图像建模方法在数据质量较差或仅有稀疏信息的情况下不足够有效。因此，我们提出了SPI-CorrNet，一种从稀疏影像数据中预测三维对应关系的统一模型。通过引入教师网络进行特征学习和适应输入方差预测来量化数据相关的不确定性。实验结果表明，我们的技术提高了稀疏图像驱动的统计形态建模的准确性和稳健性。

来自稀疏未分割图像的概率三维对应预测