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 25% in the target domain set compared to the baseline.
翻译:在医学成像界中, 域变化是一个众所周知的问题。 特别是对于内窥镜图像分析, 数据可以具有不同模式, 深度学习( DL) 方法的表现会受到不利影响。 换句话说, 在一个模式上开发的方法不能用于不同模式。 但是, 在真正的临床环境中, 内窥镜师可以将不同模式转换为更好的混凝土视觉化模式。 在本文中, 我们探索域的概括化技术, 以便能够在这样的情景中使用 DL 方法。 对于此扩展, 我们提议使用由简单线性线性迭代聚合( SLIC) 生成的超级像素。 我们用 SUPRA 方法来验证SUPERPISl Pixel 增强方法的“ SUPRA” 。 SUPRA 最初生成了一种初步的分解掩码掩码, 利用我们的新损失“ SILLLO” 和颜色- concon- concol- contropy (BE) 结合时, 我们提议使用显示显著域变换的数据。 我们验证了“ U- RA 样” 域域域域图状图状, 我们用“ U- 基 样” 基图的UDUDA 样显示了“ EDADADUDA” 基 基图, 我们用“ 基 的UDUDUDA” 基 的图图图, 。