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:领域偏移是医学图像分析领域中一个众所周知的问题。特别是在内窥镜图像分析中,由于数据可能具有不同的模态,深度学习(DL)方法的性能会受到负面影响。换句话说,在一个模态上开发的方法不能用于不同的模态。然而,在实际临床环境中,内窥镜医生会切换模态以获得更好的粘膜成像。在本文中,我们探索领域通用技术,使DL方法能够在这种情况下使用。为此,我们建议使用使用Simple Linear Iterative Clustering(SLIC)生成的超像素,将其称为“SUPRA” for SUPeRpixel Augmented method。SUPRA首先生成初步分割掩模,利用我们的新损失“ SLICLoss ”,该损失既鼓励准确的分割,又鼓励颜色一致性。我们证明,当与Binary Cross Entropy loss(BCE)相结合时,SLICLoss可以提高模型对具有显着领域偏移的数据的通用性。我们在EndoUDA数据集上使用基础U-Net验证了这种新颖的复合损失,其中包含两种模态的Barret食管和息肉的图像。我们表明,与基线相比,我们的方法可以使目标域集合的性能提高近20%。