The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible failure. This can be problematic for large-scale image segmentation applications, where data are heterogeneous and nnU-Net may fail without notice. In this work, we introduce a novel method to estimate nnU-Net uncertainty for medical image segmentation. We propose a highly effective scheme for posterior sampling of weight space for Bayesian uncertainty estimation. Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use. Additionally, we boost the segmentation performance over the original nnU-Net via marginalizing multi-modal posterior models. We applied our method on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved uncertainty estimation over a range of baseline methods. The proposed method further strengthens nnU-Net for medical image segmentation in terms of both segmentation accuracy and quality control.

翻译：自配置的nnU-Net在众多医学图像分割挑战中取得了领先性能，被广泛视为首选模型和医学图像分割的强基线方法。然而，尽管其性能卓越，nnU-Net并未提供不确定性度量来指示可能的失败。这在数据异质性强且nnU-Net可能无预警失败的大规模图像分割应用中可能引发问题。本文提出了一种新的医学图像分割中nnU-Net不确定性估计方法。我们设计了一种高效机制，用于贝叶斯不确定性估计的权重空间后验采样。与蒙特卡洛Dropout和平均场贝叶斯神经网络等现有基线方法不同，本方法无需变分框架，保持原始nnU-Net架构不变，从而保留其卓越性能与易用性。此外，我们通过边缘化多模态后验模型，在原始nnU-Net基础上提升了分割性能。在公开的心脏MRI数据集ACDC和M&M上的实验表明，本方法在不确定性估计方面优于多种基线方法，进一步增强了nnU-Net在医学图像分割中的精度与质量控制能力。