Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer variation NetwOrk (Pionono). It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions. The model is optimized by variational inference and can be trained end-to-end. It outperforms state-of-the-art models such as STAPLE, Probabilistic U-Net, and models based on confusion matrices. Additionally, Pionono predicts multiple coherent segmentation maps that mimic the rater's expert opinion, which provides additional valuable information for the diagnostic process. Experiments on real-world cancer segmentation datasets demonstrate the high accuracy and efficiency of Pionono, making it a powerful tool for medical image analysis.

翻译：医学图像分割是一项具有挑战性的任务，尤其体现在医学专家之间也存在观察者间与观察者内的变异性。本文提出了一种名为Pionono（Probabilistic Inter-Observer and iNtra-Observer variation NetwOrk，概率性观察者间与观察者内变异网络）的新型模型。该模型通过多维概率分布捕捉每位标注者的标注行为，并将此信息与图像特征图相融合，以生成概率性分割预测。模型采用变分推断进行优化，可实现端到端训练。其性能优于STAPLE、Probabilistic U-Net及基于混淆矩阵的模型等当前最先进方法。此外，Pionono可生成多个连贯的分割图，模拟标注者的专家意见，为诊断过程提供额外有价值的信息。在真实癌症分割数据集上的实验表明，Pionono具有高精度和高效率，是医学图像分析领域的强大工具。