# SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy

arXiv: https://arxiv.org/abs/2211.04658 · HTML: https://mlsanity.com/paper/2211.04658/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey](https://mlsanity.com/paper/2112.03241/) (2021)
- [Transferring and Regularizing Prediction for Semantic Segmentation](https://mlsanity.com/paper/2006.06570/) (2020)
- [Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion](https://mlsanity.com/paper/2111.04911/) (2021)
- [Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion](https://mlsanity.com/paper/2008.11946/) (2020)
- [Learning the Roots of Visual Domain Shift](https://mlsanity.com/paper/1607.06144/) (2016)
- [Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2107.01079/) (2021)
- [EndoUDA: A modality independent segmentation approach for endoscopy imaging](https://mlsanity.com/paper/2107.05342/) (2021)
- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)

## Built on by (most cited first, 3 of 3)

- [Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency](https://mlsanity.com/paper/2409.12450/) (2024)
- [Tackling domain generalization for out-of-distribution endoscopic imaging](https://mlsanity.com/paper/2410.14821/) (2024)
- [SP 3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation](https://mlsanity.com/paper/2411.11636/) (2024)

## Abstract (arXiv)

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.