Prototype Learning for Out-of-Distribution Polyp Segmentation
Nikhil Kumar Tomar Debesh Jha Ulas Bagci
Existing polyp segmentation models from colonoscopy images often fail to
provide reliable segmentation results on datasets from different centers,
limiting their applicability. Our objective in this study is to create a robust
and well-generalized segmentation model named PrototypeLab that can assist in
polyp segmentation. To achieve this, we incorporate various lighting modes such
as White light imaging (WLI), Blue light imaging (BLI), Linked color imaging
(LCI), and Flexible spectral imaging color enhancement (FICE) into our new
segmentation model, that learns to create prototypes for each class of object
present in the images. These prototypes represent the characteristic features
of the objects, such as their shape, texture, color. Our model is designed to
perform effectively on out-of-distribution (OOD) datasets from multiple
centers. We first generate a coarse mask that is used to learn prototypes for
the main object class, which are then employed to generate the final
segmentation mask. By using prototypes to represent the main class, our
approach handles the variability present in the medical images and generalize
well to new data since prototype capture the underlying distribution of the
data. PrototypeLab offers a promising solution with a dice coefficient of
$\geq$ 90\% and mIoU $\geq$ 85\% with a near real-time processing speed for
polyp segmentation. It achieved superior performance on OOD datasets compared
to 16 state-of-the-art image segmentation architectures, potentially improving
clinical outcomes. Codes are available at
https://github.com/xxxxx/PrototypeLab.