Purpose

Estimating uncertainty in predictions made by neural networks is critically important for increasing the trust medical experts have in automatic data analysis results. In segmentation tasks, quantifying levels of confidence can provide meaningful additional information to aid clinical decision making. In recent work, we proposed an interpretable uncertainty measure to aid clinicians in assessing the reliability of developmental dysplasia of the hip metrics measured from 3D ultrasound screening scans, as well as that of the US scan itself. In this work, we propose a technique to quantify confidence in the associated segmentation process that incorporates voxel-wise uncertainty into the binary loss function used in the training regime, which encourages the network to concentrate its training effort on its least certain predictions.

Methods

We propose using a Bayesian-based technique to quantify 3D segmentation uncertainty by modifying the loss function within an encoder-decoder type voxel labeling deep network. By appending a voxel-wise uncertainty measure, our modified loss helps the network improve prediction uncertainty for voxels that are harder to train. We validate our approach by training a Bayesian 3D U-Net with the proposed modified loss function on a dataset comprising 92 clinical 3D US neonate scans and test on a separate hold-out dataset of 24 patients.

Results

Quantitatively, we show that the Dice score of ilium and acetabulum segmentation improves by 5% when trained with our proposed voxel-wise uncertainty loss compared to training with standard cross-entropy loss. Qualitatively, we further demonstrate how our modified loss function results in meaningful reduction of voxel-wise segmentation uncertainty estimates, with the network making more confident accurate predictions.

Conclusion

We proposed a Bayesian technique to encode voxel-wise segmentation uncertainty information into deep neural network optimization, and demonstrated how it can be leveraged into meaningful confidence measures to improve the model’s predictive performance.

中文翻译：

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利用体素分割不确定性提高小儿髋关节发育不良评估的可靠性

目的

估计神经网络预测的不确定性对于提高医学专家对自动数据分析结果的信任至关重要。在分割任务中，量化置信水平可以提供有意义的附加信息，以帮助临床决策。在最近的工作中，我们提出了一种可解释的不确定性测量方法，以帮助临床医生评估从 3D 超声筛查扫描以及美国扫描本身测量的髋关节发育不良指标的可靠性。在这项工作中，我们提出了一种量化相关分割过程的置信度的技术，该技术将体素不确定性纳入训练方案中使用的二元损失函数中，这鼓励网络将其训练工作集中在其最不确定的预测上。

方法

我们建议使用基于贝叶斯的技术通过修改编码器-解码器类型体素标记深度网络中的损失函数来量化 3D 分割不确定性。通过附加体素不确定性度量，我们修改后的损失有助于网络改善难以训练的体素的预测不确定性。我们通过在包含 92 个临床 3D US 新生儿扫描的数据集上训练贝叶斯 3D U-Net 来验证我们的方法，并在 24 名患者的单独保留数据集上进行测试。

结果

定量地，我们表明，与使用标准交叉熵损失进行训练相比，使用我们提出的体素不确定性损失进行训练时，髂骨和髋臼分割的 Dice 得分提高了 5%。定性地，我们进一步展示了我们修改后的损失函数如何导致体素分割不确定性估计的有意义的减少，同时网络做出更自信的准确预测。

结论

我们提出了一种贝叶斯技术，将体素分割不确定性信息编码到深度神经网络优化中，并展示了如何将其用于有意义的置信度度量以提高模型的预测性能。