3D解剖分割中切片传播不确定性的估计与分析
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
AI总结：
针对3D解剖分割自监督切片传播方法预测可信度不足的问题，提出集成校准不确定性量化的方案，经多数据集多方法实验验证可提升可信度与分割准确率，并揭示潜在失效模式。
摘要：
3D解剖分割的监督方法表现出优异性能，但往往受限于标注数据的可用性。这一局限结合大量可用的未标注数据，引发了人们对自监督方法日益增长的兴趣。切片传播作为一种自监督方法应运而生，它将切片配准作为自监督任务，以最少的监督实现完整解剖分割。该方法大幅降低了对领域专业知识、时间的需求，也减少了构建训练分割网络所需的全标注数据集的相关成本。然而，这种通过确定性网络减少监督的转变，引发了人们对预测可信度与可靠性的担忧，尤其是与更准确的监督方法相比时。为解决这一问题，我们提出将校准后的不确定性量化（UQ）集成到切片传播方法中，为模型的预测可靠性与置信水平提供洞察。纳入不确定性度量可提升用户对自监督方法的信心，从而提高其实际适用性。我们在三个3D腹部分割数据集上使用五种UQ方法开展了实验。结果表明，纳入UQ不仅提升了模型的可信度，还提高了分割准确率。此外，我们的分析揭示了切片传播方法的多种失效模式，这些模式可能对终端用户而言并非一目了然。本研究为提升切片传播方法的准确率与可信度开辟了新的研究方向。
英文摘要：
Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data. Slice propagation has emerged as an self-supervised approach that leverages slice registration as a self-supervised task to achieve full anatomy segmentation with minimal supervision. This approach significantly reduces the need for domain expertise, time, and the cost associated with building fully annotated datasets required for training segmentation networks. However, this shift toward reduced supervision via deterministic networks raises concerns about the trustworthiness and reliability of predictions, especially when compared with more accurate supervised approaches. To address this concern, we propose the integration of calibrated uncertainty quantification (UQ) into slice propagation methods, providing insights into the model's predictive reliability and confidence levels. Incorporating uncertainty measures enhances user confidence in self-supervised approaches, thereby improving their practical applicability. We conducted experiments on three datasets for 3D abdominal segmentation using five UQ methods. The results illustrate that incorporating UQ improves not only model trustworthiness, but also segmentation accuracy. Furthermore, our analysis reveals various failure modes of slice propagation methods that might not be immediately apparent to end-users. This study opens up new research avenues to improve the accuracy and trustworthiness of slice propagation methods.
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