论文标题

不确定深:从图像到概率形状模型

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

论文作者

Adams, Jadie, Bhalodia, Riddhish, Elhabian, Shireen

论文摘要

统计形状建模(SSM)最近利用了深度学习的进步,以减轻对解剖学分割的耗时和专家驱动的工作流程的需求,形状注册和人口级形状表示的优化。 DeepSM是一种端到端的深度学习方法,它直接从没有手动开销的未分段图像中提取统计形状表示。它使用最先进的形状建模方法可行,用于估计对后续下游任务可行的形态。尽管如此,DeepSM会产生过度自信的形状估计值,而不能盲目地认为是准确的。因此,通过量化不确定性的颗粒状估计来传达DeepSM所不知道的,对于将其作为按需诊断工具的直接临床应用至关重要,以确定模型输出的信任程度。在这里,我们提出了不确定的深色作为一个统一模型,它通过调整网络来预测固有的输入差异,以及通过蒙特卡洛辍学采样来预测固有的输入差异,从而量化数据依赖于数据的息肉不确定性,并通过模型依赖性的认知不确定性,以近似于网络参数的变量分布。实验显示了对DeepSM的准确性提高,同时保持了几乎没有预处理的端到端的相同好处。

Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations. DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images with little manual overhead. It performs comparably with state-of-the-art shape modeling methods for estimating morphologies that are viable for subsequent downstream tasks. Nonetheless, DeepSSM produces an overconfident estimate of shape that cannot be blindly assumed to be accurate. Hence, conveying what DeepSSM does not know, via quantifying granular estimates of uncertainty, is critical for its direct clinical application as an on-demand diagnostic tool to determine how trustworthy the model output is. Here, we propose Uncertain-DeepSSM as a unified model that quantifies both, data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variance, and model-dependent epistemic uncertainty via a Monte Carlo dropout sampling to approximate a variational distribution over the network parameters. Experiments show an accuracy improvement over DeepSSM while maintaining the same benefits of being end-to-end with little pre-processing.

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