论文标题
集合不确定性作为数据集扩展的标准，以不同的骨骼分割与上身CT图像的不同
Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images
论文作者
论文摘要
目的：在许多计划和导航应用程序中，单个骨骼的本地化和分割是重要的预处理步骤。但是，如果手动完成，这是一项耗时和重复的任务。这不仅对于临床实践，而且对于获取培训数据都是正确的。因此，我们不仅提出了一种端到端学习的算法，该算法能够在上身CT中分割125个不同的骨骼，而且还提供了基于合奏的不确定性度量，该度量有助于单张扫描以扩大训练数据集。我们使用受3D-UNET和完全监督培训启发的神经网络体系结构创建全自动的端到端学习分段。使用合奏和推理时间扩大改进结果。我们研究了合奏 - 不确定性与未标记的扫描的前瞻性用途，这是培训数据集的一部分。结果：我们的方法在16个上体CT扫描的内部数据集上进行了评估，每个维度的分辨率为\ si {2} {\ milli \ meter}。考虑到我们的标签集中的所有125个骨头，我们最成功的合奏达到了中位数骰子得分系数为0.83。我们发现扫描的集合不确定性与其对扩大训练集中获得的准确性的前瞻性影响之间缺乏相关性。同时，我们表明，集成不确定性与初始自动分割后需要手动校正的体素数量相关，从而最大程度地减少了最终确定新的地面真实分段所需的时间。结论：结合结合，集合不确定性低的扫描需要更少的注释时间，同时产生类似的将来的DSC改进。因此，它们是从CT扫描中扩大上身不同骨分割的训练集的理想候选者。 }
Purpose: The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications. It is, however, a time-consuming and repetitive task if done manually. This is true not only for clinical practice but also for the acquisition of training data. We therefore not only present an end-to-end learnt algorithm that is capable of segmenting 125 distinct bones in an upper-body CT, but also provide an ensemble-based uncertainty measure that helps to single out scans to enlarge the training dataset with. Methods We create fully automated end-to-end learnt segmentations using a neural network architecture inspired by the 3D-Unet and fully supervised training. The results are improved using ensembles and inference-time augmentation. We examine the relationship of ensemble-uncertainty to an unlabelled scan's prospective usefulness as part of the training dataset. Results: Our methods are evaluated on an in-house dataset of 16 upper-body CT scans with a resolution of \SI{2}{\milli\meter} per dimension. Taking into account all 125 bones in our label set, our most successful ensemble achieves a median dice score coefficient of 0.83. We find a lack of correlation between a scan's ensemble uncertainty and its prospective influence on the accuracies achieved within an enlarged training set. At the same time, we show that the ensemble uncertainty correlates to the number of voxels that need manual correction after an initial automated segmentation, thus minimising the time required to finalise a new ground truth segmentation. Conclusion: In combination, scans with low ensemble uncertainty need less annotator time while yielding similar future DSC improvements. They are thus ideal candidates to enlarge a training set for upper-body distinct bone segmentation from CT scans. }
