Current state-of-the-art deep learning segmentation methods have not yet made
a broad entrance into the clinical setting in spite of high demand for such
automatic methods. One important reason is the lack of reliability caused by
models that fail unnoticed and often locally produce anatomically implausible
results that medical experts would not make. This paper presents an automatic
image segmentation method based on (Bayesian) dilated convolutional networks
(DCNN) that generate segmentation masks and spatial uncertainty maps for the
input image at hand. The method was trained and evaluated using segmentation of
the left ventricle (LV) cavity, right ventricle (RV) endocardium and myocardium
(Myo) at end-diastole (ED) and end-systole (ES) in 100 cardiac 2D MR scans from
the MICCAI 2017 Challenge (ACDC). Combining segmentations and uncertainty maps
and employing a human-in-the-loop setting, we provide evidence that image areas
indicated as highly uncertain regarding the obtained segmentation almost
entirely cover regions of incorrect segmentations. The fused information can be
harnessed to increase segmentation performance. Our results reveal that we can
obtain valuable spatial uncertainty maps with low computational effort using
DCNNs.

本文提出了一种自动图像分割方法，基于 (Bayesian) 扩张卷积神经网络 (DCNN)，用于自动生成手头输入图像的分割掩模和空间不确定性地图，并利用人机交互设置结合分割和不确定性图来提高分割性能。该方法使用 ACDC 的 100 个心脏 2D MR 扫描中的左室腔、右室内膜和心肌 ED 和 ES 进行训练和评估，结果表明，可以使用 DCNN 获得低计算成本的有价值的空间不确定性图。

提高心脏 MRI 深度学习分割方法的可信度

Towards increased trustworthiness of deep learning segmentation methods  on cardiac MRI

The use of deep learning for medical imaging has seen tremendous growth in
the research community. One reason for the slow uptake of these systems in the
clinical setting is that they are complex, opaque and tend to fail silently.
Outside of the medical imaging domain, the machine learning community has
recently proposed several techniques for quantifying model uncertainty (i.e.~a
model knowing when it has failed). This is important in practical settings, as
we can refer such cases to manual inspection or correction by humans. In this
paper, we aim to bring these recent results on estimating uncertainty to bear
on two important outputs in deep learning-based segmentation. The first is
producing spatial uncertainty maps, from which a clinician can observe where
and why a system thinks it is failing. The second is quantifying an image-level
prediction of failure, which is useful for isolating specific cases and
removing them from automated pipelines. We also show that reasoning about
spatial uncertainty, the first output, is a useful intermediate representation
for generating segmentation quality predictions, the second output. We propose
a two-stage architecture for producing these measures of uncertainty, which can
accommodate any deep learning-based medical segmentation pipeline.

本文旨在将最近关于评估不确定性的结果应用于基于深度学习的医学分割中的两个重要输出：产生空间不确定性映射，以便临床医生可以观察系统何时以及为何错误，以及量化图像级别的失败预测，并展示关于空间不确定性的推理是产生分割质量预测的有用中间表示。提出了一个两阶段的架构来生成这些不确定性测量，可以适应任何基于深度学习的医学分割管道。