论文标题
清脆 - 医学图像细分的可靠不确定性估计
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
论文作者
论文摘要
准确的不确定性估计是医学成像社区的关键需求。已经提出了多种方法，这些方法的所有直接扩展是分类不确定性估计技术的所有直接扩展。独立像素的不确定性估计通常基于神经网络的概率解释，不考虑解剖学的先验知识，因此为许多分割任务提供了次优的结果。因此，我们提出了不确定性预测方法的对比度图像分割。 Crisp以其核心实现了一种对比的方法，以学习一个编码有效分割及其相应图像的分布的联合潜在空间。我们使用此联合潜在空间将预测与数千个潜在矢量进行比较，并提供解剖学上一致的不确定性图。在涉及不同方式和器官的四个医学图像数据库上进行的全面研究强调了我们方法的优越性与最先进的方法相比。
Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. The independent pixel-wise uncertainty estimates, often based on the probabilistic interpretation of neural networks, do not take into account anatomical prior knowledge and consequently provide sub-optimal results to many segmentation tasks. For this reason, we propose CRISP a ContRastive Image Segmentation for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations and their corresponding images. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps. Comprehensive studies performed on four medical image databases involving different modalities and organs underlines the superiority of our method compared to state-of-the-art approaches.
