Advancements in image segmentation play an integral role within the greater scope of Deep Learning-based computer vision. Furthermore, their widespread applicability in critical real-world tasks has given rise to challenges related to the reliability of such algorithms. Hence, uncertainty quantification has been extensively studied within this context, enabling expression of model ignorance (epistemic uncertainty) or data ambiguity (aleatoric uncertainty) to prevent uninformed decision making. Due to the rapid adoption of Convolutional Neural Network (CNN)-based segmentation models in high-stake applications, a substantial body of research has been published on this very topic, causing its swift expansion into a distinct field. This work provides a comprehensive overview of probabilistic segmentation by discussing fundamental concepts in uncertainty that govern advancements in the field as well as the application to various tasks. We identify that quantifying aleatoric and epistemic uncertainty approximates Bayesian inference w.r.t. to either latent variables or model parameters, respectively. Moreover, literature on both uncertainties trace back to four key applications; (1) to quantify statistical inconsistencies in the annotation process due ambiguous images, (2) correlating prediction error with uncertainty, (3) expanding the model hypothesis space for better generalization, and (4) active learning. Then, a discussion follows that includes an overview of utilized datasets for each of the applications and comparison of the available methods. We also highlight challenges related to architectures, uncertainty-based active learning, standardization and benchmarking, and recommendations for future work such as methods based on single forward passes and models that appropriately leverage volumetric data.

本研究解决了深度学习图像分割算法在可靠性方面的挑战，特别是在高风险应用中的不确定性量化问题。本文提出将贝叶斯推断与不确定性量化相结合，提供了一种新的视角，并对四个关键应用进行了深入分析。最重要的发现是，不确定性量化可以提高模型的决策能力，从而增强图像分割的整体效果和可靠性。

深度概率图像分割中的贝叶斯不确定性量化综述