鉴于结肠镜检查图像中息肉的精确分割在自动化结直肠癌诊断中的关键作用，近年来它受到了广泛的关注。许多现有的基于深度学习的方法遵循单阶段处理流程，通常涉及不同级别的特征融合或利用与边界相关的注意机制。借鉴迭代反馈单元（IFU）在图像息肉分割中应用的成功，本文通过将IFU扩展到视频息肉分割领域提出了FlowICBNet。通过利用 IFU 的独特功能来传播和完善过去的分割结果，我们的方法被证明可以有效缓解与内窥镜成像固有局限性相关的挑战，特别是频繁出现的相机抖动和帧散焦。此外，在 FlowICBNet 中，我们引入了两个关键模块：参考系选择（RFS）和流引导变形（FGW）。这些模块在过滤和选择最适合手头任务的历史参考系方面发挥着至关重要的作用。在大型视频息肉分割数据集上的实验结果表明，我们的方法可以显着优于最先进的方法，在 SUN-SEG-Easy 上实现平均指标改进 7.5%，在 SUN-SEG-SUN-SEG- 上实现平均指标改进 7.4%。难的。我们的代码可在 https://github.com/eraserNut/ICBNet 上获取。版权所有 © 2024。由 Elsevier Ltd 发布。

Accurate segmentation of polyps in colonoscopy images has gained significant attention in recent years, given its crucial role in automated colorectal cancer diagnosis. Many existing deep learning-based methods follow a one-stage processing pipeline, often involving feature fusion across different levels or utilizing boundary-related attention mechanisms. Drawing on the success of applying Iterative Feedback Units (IFU) in image polyp segmentation, this paper proposes FlowICBNet by extending the IFU to the domain of video polyp segmentation. By harnessing the unique capabilities of IFU to propagate and refine past segmentation results, our method proves effective in mitigating challenges linked to the inherent limitations of endoscopic imaging, notably the presence of frequent camera shake and frame defocusing. Furthermore, in FlowICBNet, we introduce two pivotal modules: Reference Frame Selection (RFS) and Flow Guided Warping (FGW). These modules play a crucial role in filtering and selecting the most suitable historical reference frames for the task at hand. The experimental results on a large video polyp segmentation dataset demonstrate that our method can significantly outperform state-of-the-art methods by notable margins achieving an average metrics improvement of 7.5% on SUN-SEG-Easy and 7.4% on SUN-SEG-Hard. Our code is available at https://github.com/eraserNut/ICBNet.Copyright © 2024. Published by Elsevier Ltd.