Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation.

Rachaell Nihalaani, Tushar Kataria, Jadie Adams, Shireen Y Elhabian
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Abstract

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 unannotated data. Slice propagation has emerged as a 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 integrating calibrated uncertainty quantification (UQ) into slice propagation methods, which would provide 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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三维解剖分割中切片传播不确定性的估计与分析。
用于三维解剖分割的监督方法表现出卓越的性能,但往往受到注释数据可用性的限制。这种局限性导致人们对自监督方法以及大量可用的未注释数据越来越感兴趣。切片传播是一种自我监督方法,它利用切片配准作为一项自我监督任务,以最少的监督实现全面解剖分割。这种方法大大减少了对领域专业知识的需求、时间,以及与建立训练分割网络所需的完全注释数据集相关的成本。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和可靠性的担忧,尤其是与更精确的监督方法相比。为了解决这个问题,我们建议将校准的不确定性量化(UQ)整合到切片传播方法中,从而深入了解模型的预测可靠性和置信度。纳入不确定性度量可增强用户对自我监督方法的信心,从而提高其实际应用性。我们在三个数据集上使用五种 UQ 方法进行了三维腹部分割实验。结果表明,纳入 UQ 不仅能提高模型的可信度,还能提高分割的准确性。此外,我们的分析还揭示了切片传播方法的各种失效模式,而这些失效模式对于最终用户来说可能并不是显而易见的。这项研究为提高切片传播方法的准确性和可信度开辟了新的研究途径。
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