通过解耦风格-内容信息与超像素一致性实现内镜图像分割的域泛化
Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency
- School of Engineering and Sciences Tecnologico de Monterrey（蒙特雷理工学院工程与科学学院）
- School of Computing University of Leeds（利兹大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对内镜图像分割的域泛化问题，提出结合超像素一致性（SUPRA）与实例归一化及实例选择性白化（ISW）进行风格-内容解耦的方法，在息肉和Barrett食管数据集上显著优于基线和现有SOTA方法。
AI中文摘要：
频繁的监测对于根据个体患胃肠道（GI）癌前病变的可能性进行分层是必要的。在临床实践中，白光成像（WLI）以及窄带成像（NBI）和荧光成像等补充模态被用于评估风险区域。然而，当模型在一种模态上训练并在另一种模态上测试时，由于域差距，传统的深度学习（DL）模型表现出性能下降。在我们早期的方法中，我们使用了一种基于超像素的方法，称为“SUPRA”，通过利用颜色和空间距离来生成像素组，从而有效学习域不变信息。早期工作的主要局限性之一是聚合过程未利用结构信息，使其在分割任务中表现次优，尤其是对于息肉和异质性颜色分布。因此，在本工作中，我们提出了一种使用实例归一化和实例选择性白化（ISW）进行风格-内容解耦的方法，结合SUPRA以改进域泛化。我们在两个数据集上评估了我们的方法：EndoUDA Barrett食管和EndoUDA息肉，并将其性能与三种最先进（SOTA）方法进行比较。我们的研究结果表明，在目标域数据上，与基线和SOTA方法相比，性能显著提升。具体而言，在息肉数据集上，我们的方法相较于基线和三种SOTA方法分别提升了14%、10%、8%和18%。此外，在Barrett食管数据集上，它比第二好的方法（EndoUDA）高出近2%。
英文摘要：
Frequent monitoring is necessary to stratify individuals based on their likelihood of developing gastrointestinal (GI) cancer precursors. In clinical practice, white-light imaging (WLI) and complementary modalities such as narrow-band imaging (NBI) and fluorescence imaging are used to assess risk areas. However, conventional deep learning (DL) models show degraded performance due to the domain gap when a model is trained on one modality and tested on a different one. In our earlier approach, we used a superpixel-based method referred to as "SUPRA" to effectively learn domain-invariant information using color and space distances to generate groups of pixels. One of the main limitations of this earlier work is that the aggregation does not exploit structural information, making it suboptimal for segmentation tasks, especially for polyps and heterogeneous color distributions. Therefore, in this work, we propose an approach for style-content disentanglement using instance normalization and instance selective whitening (ISW) for improved domain generalization when combined with SUPRA. We evaluate our approach on two datasets: EndoUDA Barrett's Esophagus and EndoUDA polyps, and compare its performance with three state-of-the-art (SOTA) methods. Our findings demonstrate a notable enhancement in performance compared to both baseline and SOTA methods across the target domain data. Specifically, our approach exhibited improvements of 14%, 10%, 8%, and 18% over the baseline and three SOTA methods on the polyp dataset. Additionally, it surpassed the second-best method (EndoUDA) on the Barrett's Esophagus dataset by nearly 2%.
