结肠镜图像中息肉的自动分割在结直肠癌的早期诊断和治疗中起着至关重要的作用。尽管如此，一些瓶颈仍然存在。在我们之前的工作中，我们主要关注具有类内不一致和低对比度的息肉，使用 ICGNet 来解决它们。由于设备、息肉具体位置和性质的不同，采集图像的颜色分布不一致。 ICGNet 的设计主要采用反向轮廓引导信息和局部全局上下文信息，忽略了这种不一致的颜色分布，从而导致过拟合问题，并且很难仅关注有益的图像内容。此外，值得信赖的分割模型不仅应该产生高精度结果，还应该提供伴随其预测的不确定性度量，以便医生能够做出明智的决策。然而ICGNet只给出了分割结果，缺乏不确定性度量。为了应对这些新的瓶颈，我们进一步将原始的 ICGNet 扩展为一个全面有效的网络（UM-Net），其两个主要贡献已被实验证明具有实质性的实用价值。首先，我们采用颜色转移操作来弱化颜色与息肉之间的关系，使模型更关注息肉的形状。其次，我们提供不确定性来表示分割结果的可靠性，并使用方差来校正不确定性。我们改进的方法在五个息肉数据集上进行了评估，与其他先进方法相比，在学习能力和泛化能力方面都显示出有竞争力的结果。源代码可在 https://github.com/dxqllp/UM-Net 上获取。版权所有 © 2024 Elsevier B.V。保留所有权利。

Automatic segmentation of polyps from colonoscopy images plays a critical role in the early diagnosis and treatment of colorectal cancer. Nevertheless, some bottlenecks still exist. In our previous work, we mainly focused on polyps with intra-class inconsistency and low contrast, using ICGNet to solve them. Due to the different equipment, specific locations and properties of polyps, the color distribution of the collected images is inconsistent. ICGNet was designed primarily with reverse-contour guide information and local-global context information, ignoring this inconsistent color distribution, which leads to overfitting problems and makes it difficult to focus only on beneficial image content. In addition, a trustworthy segmentation model should not only produce high-precision results but also provide a measure of uncertainty to accompany its predictions so that physicians can make informed decisions. However, ICGNet only gives the segmentation result and lacks the uncertainty measure. To cope with these novel bottlenecks, we further extend the original ICGNet to a comprehensive and effective network (UM-Net) with two main contributions that have been proved by experiments to have substantial practical value. Firstly, we employ a color transfer operation to weaken the relationship between color and polyps, making the model more concerned with the shape of the polyps. Secondly, we provide the uncertainty to represent the reliability of the segmentation results and use variance to rectify uncertainty. Our improved method is evaluated on five polyp datasets, which shows competitive results compared to other advanced methods in both learning ability and generalization capability. The source code is available at https://github.com/dxqllp/UM-Net.Copyright © 2024 Elsevier B.V. All rights reserved.