SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy
This work addresses domain generalization for endoscopic segmentation, enabling deep learning methods to handle modality switches in clinical settings, though it appears incremental as it builds on existing techniques like U-Net and SLIC.
The paper tackled the domain shift problem in endoscopic image analysis by proposing SUPRA, a superpixel-guided loss method that improves multi-modal segmentation, achieving nearly 20% improvement in the target domain compared to the baseline.
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.