Manual medical image segmentation is subjective and suffers from annotator-related bias, which can be mimicked or amplified by deep learning methods. Recently, researchers have suggested that such bias is the combination of the annotator preference and stochastic error, which are modeled by convolution blocks located after decoder and pixel-wise independent Gaussian distribution, respectively. It is unlikely that convolution blocks can effectively model the varying degrees of preference at the full resolution level. Additionally, the independent pixel-wise Gaussian distribution disregards pixel correlations, leading to a discontinuous boundary. This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors. TAB employs the Transformer with learnable queries to extract the different preference-focused features. This enables TAB to produce segmentation with various preferences simultaneously using a single segmentation head. Moreover, TAB takes the multivariant normal distribution assumption that models pixel correlations, and learns the annotation distribution to disentangle the stochastic error. We evaluated our TAB on an OD/OC segmentation benchmark annotated by six annotators. Our results suggest that TAB outperforms existing medical image segmentation models which take into account the annotator-related bias.

翻译：手工医学图像分割具有主观性，且存在标注者相关偏差，该偏差可能被深度学习方法模仿或放大。近年研究表明，此类偏差是标注者偏好与随机误差的结合，两者分别通过解码器后的卷积模块和逐像素独立高斯分布建模。然而，卷积模块难以在全分辨率层面有效建模不同程度的偏好。此外，逐像素独立高斯分布忽略了像素相关性，导致分割边界不连续。本文提出基于Transformer的标注偏差感知（TAB）医学图像分割模型，通过建模标注者偏好与随机误差来解决标注相关偏差问题。TAB采用配备可学习查询的Transformer提取不同偏好聚焦特征，使其能够通过单个分割头同步生成包含多种偏好的分割结果。同时，TAB采用多变量正态分布假设以建模像素相关性，并通过学习标注分布来解耦随机误差。我们基于六位标注者标注的OD/OC分割基准数据集进行评估，结果表明TAB优于现有考虑标注者偏差的医学图像分割模型。