Domain shift is a well-known problem in the medical imaging community. In particular, for endoscopic image analysis where the data can have different modalities the performance of deep learning (DL) methods gets adversely affected. In other words, methods developed on one modality cannot be used for a different modality. However, in real clinical settings, endoscopists switch between modalities for better mucosal visualisation. In this paper, we explore the domain generalisation technique to enable DL methods to be used in such scenarios. To this extend, we propose to use super pixels generated with Simple Linear Iterative Clustering (SLIC) which we refer to as "SUPRA" for SUPeRpixel Augmented method. SUPRA first generates a preliminary segmentation mask making use of our new loss "SLICLoss" that encourages both an accurate and color-consistent segmentation. We demonstrate that SLICLoss when combined with Binary Cross Entropy loss (BCE) can improve the model's generalisability with data that presents significant domain shift. We validate this novel compound loss on a vanilla U-Net using the EndoUDA dataset, which contains images for Barret's Esophagus and polyps from two modalities. We show that our method yields an improvement of nearly 20% in the target domain set compared to the baseline.

翻译：域偏移是医学影像领域公认的问题。尤其在内窥镜图像分析中，数据可能来自不同模态，深度学习方法性能会受到不利影响。换言之，针对某一模态开发的方法无法适用于其他模态。然而在临床实际环境中，内窥镜医师常需切换模态以获得更清晰的黏膜可视化效果。本文探索域泛化技术以使深度学习方法适应此类场景，为此提出利用简单线性迭代聚类（SLIC）生成超像素的方法，称为"SUPRA"（超像素增强方法）。SUPRA通过新提出的损失函数"SLICLoss"生成初始分割掩膜，该损失函数兼顾分割准确性与色彩一致性。实验表明，将SLICLoss与二元交叉熵损失（BCE）结合使用时，可提升模型在存在显著域偏移数据上的泛化能力。我们使用EndoUDA数据集（包含巴雷特食管和息肉两种模态的图像）在标准U-Net架构上验证了该复合损失函数的有效性。结果显示，与基线方法相比，本方法在目标域数据集上提升了近20%的性能。