Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency

Frequent monitoring is necessary to stratify individuals based on their likelihood of developing gastrointestinal (GI) cancer precursors. In the clinical practice, white-light imaging (WLI), and complimentary modalities such as narrow-band imaging (NISI) and fluorescence imaging are used to assess risk areas. However, conventional deep learning (DL) models have depleted performance due to domain gap when a model is trained on one modality and tested on a different one. In our earlier approach we used superpixel based method referred to as "SUPRA" to effectively learn domain-invariant information using color and space distances to generate groups of pixels. One of the main limitations of this early work is that the aggregation does not exploit structural information, making it sub-optimal for segmentation tasks, especially for polyps and heterogeneous color distributions. Therefore, in this work, we propose an approach for style-content disentanglement using instance normalization and instance selective whitening (ISW) for an improved domain generalization when combined with SUPRA. We evaluate our approach on two datasets: EndoUDA Barret's Esophagus and EndoUDA polyps and compare its performance with previous three stale-of-the-art (SOTA) methods. Our findings demonstrate a notable enhancement in performance compared to both baseline and state-of-the-art methods across the target domain data. Specifically, our approach exhibited improvements of 14 %, 10%, 8%, and 18% over the baseline and three SOTA methods on the polyp dataset. Additionally, it surpassed the second best method (EndoUDA) on the BE dataset by nearly 2%.