Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work. First, acquiring complete pixel-level segmentation labels for medical images is time-consuming and requires domain expertise. Second, typical segmentation pipelines cannot detect out-of-distribution (OOD) pixels, leaving them prone to spurious outputs during deployment. In this work, we propose a novel segmentation approach exploiting OOD detection that learns only from sparsely annotated pixels from multiple positive-only classes. These multi-class positive annotations naturally fall within the in-distribution (ID) set. Unlabelled pixels may contain positive classes but also negative ones, including what is typically referred to as \emph{background} in standard segmentation formulations. Here, we forgo the need for background annotation and consider these together with any other unseen classes as part of the OOD set. Our framework can integrate, at a pixel-level, any OOD detection approaches designed for classification tasks. To address the lack of existing OOD datasets and established evaluation metric for medical image segmentation, we propose a cross-validation strategy that treats held-out labelled classes as OOD. Extensive experiments on both multi-class hyperspectral and RGB surgical imaging datasets demonstrate the robustness and generalisation capability of our proposed framework.

本文针对医学和外科成像中的深度神经网络分割面临的挑战，提出了一种利用异构检测的新型分割方法，该方法仅使用稀疏注释的多类正标签进行学习，解决了获取完整像素级标注的时间成本及缺乏对异构像素检测的问题。研究结果表明，该框架在多类超光谱和RGB外科成像数据集上的实验展示了其鲁棒性和广泛的推广能力。

OOD-SEG：基于稀疏多类正标签的图像分割的异构检测