Stochastic Segmentation Networks：空间相关随机不确定性建模
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
摘要：
在图像分割中，对于一个给定的输入，通常有不止一个合理的解决方案。例如，在医学成像中，专家们经常会对物体边界的确切位置产生分歧。估计这种固有的不确定性并预测多个合理的假设在许多应用中具有很大的意义，但这种能力在大多数当前的深度学习方法中都缺乏。在本文中，我们介绍了随机分割网络（SSNs），一个有效的概率方法建模任意的不确定性与任何图像分割网络架构。与产生逐像素估计的方法相比，SSN对整个标签映射的联合分布进行建模，从而可以为单个图像生成多个空间相干假设。通过在logit空间上使用低秩多元正态分布来模拟给定图像的标签映射的概率，我们获得了空间一致的概率分布，该概率分布可以由神经网络有效地计算，而无需对底层架构进行任何更改。我们测试了我们的方法对现实世界的医疗数据，包括肺结节在2D CT和脑肿瘤在3D多模态MRI扫描分割。SSN在对模糊图像中的相关不确定性进行建模方面优于最先进的技术，同时更简单，更灵活，更高效。
英文摘要：
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of object boundaries. Estimating this inherent uncertainty and predicting multiple plausible hypotheses is of great interest in many applications, yet this ability is lacking in most current deep learning methods. In this paper, we introduce stochastic segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. In contrast to approaches that produce pixel-wise estimates, SSNs model joint distributions over entire label maps and thus can generate multiple spatially coherent hypotheses for a single image. By using a low-rank multivariate normal distribution over the logit space to model the probability of the label map given the image, we obtain a spatially consistent probability distribution that can be efficiently computed by a neural network without any changes to the underlying architecture. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans. SSNs outperform state-of-the-art for modelling correlated uncertainty in ambiguous images while being much simpler, more flexible, and more efficient.
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