  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.
