Existing polyp segmentation models from colonoscopy images often fail to provide reliable segmentation results on datasets from different centers, limiting their applicability. Our objective in this study is to create a robust and well-generalized segmentation model named PrototypeLab that can assist in polyp segmentation. To achieve this, we incorporate various lighting modes such as White light imaging (WLI), Blue light imaging (BLI), Linked color imaging (LCI), and Flexible spectral imaging color enhancement (FICE) into our new segmentation model, that learns to create prototypes for each class of object present in the images. These prototypes represent the characteristic features of the objects, such as their shape, texture, color. Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers. We first generate a coarse mask that is used to learn prototypes for the main object class, which are then employed to generate the final segmentation mask. By using prototypes to represent the main class, our approach handles the variability present in the medical images and generalize well to new data since prototype capture the underlying distribution of the data. PrototypeLab offers a promising solution with a dice coefficient of $\geq$ 90\% and mIoU $\geq$ 85\% with a near real-time processing speed for polyp segmentation. It achieved superior performance on OOD datasets compared to 16 state-of-the-art image segmentation architectures, potentially improving clinical outcomes. Codes are available at https://github.com/xxxxx/PrototypeLab.

翻译：现有基于结肠镜图像的息肉分割模型在不同中心的数据集上往往无法提供可靠的分割结果，限制了其应用范围。本研究旨在构建一个名为PrototypeLab的鲁棒且泛化能力强的分割模型，以辅助息肉分割。为此，我们将多种光照模式（如白光成像、蓝光成像、联动色彩成像及柔性分光色彩增强）融入新分割模型，该模型通过学习为图像中每类目标创建原型。这些原型表征了目标的特征属性，例如形状、纹理和颜色。我们的模型设计旨在对来自多个中心的分布外（OOD）数据集实现有效分割。首先，我们生成一个粗掩模，用于学习主目标类别的原型，进而利用这些原型生成最终分割掩模。通过使用原型表示主类别，该方法能够处理医学图像中的变异性，并良好泛化至新数据，因为原型捕捉了数据的底层分布。PrototypeLab在息肉分割中提供了具有前景的解决方案，其Dice系数≥90%，mIoU≥85%，且具备近实时处理速度。与16种最先进图像分割架构相比，它在OOD数据集上取得了更优性能，有望改善临床结局。代码可访问https://github.com/xxxxx/PrototypeLab获取。