On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation

TL;DR

This paper evaluates the impact of rotation-equivariance in U-Net architectures for image segmentation, analyzing performance improvements and computational costs across diverse datasets with arbitrary object orientations.

Contribution

It provides a comprehensive benchmark of rotation-equivariant U-Net models, highlighting their benefits and trade-offs in various segmentation tasks beyond specific applications.

Findings

Rotation-equivariant U-Nets improve segmentation accuracy on arbitrarily oriented objects.

Increased computational cost is observed with rotation-equivariance.

Performance gains vary depending on dataset and task complexity.

Abstract

Numerous studies have recently focused on incorporating different variations of equivariance in Convolutional Neural Networks (CNNs). In particular, rotation-equivariance has gathered significant attention due to its relevance in many applications related to medical imaging, microscopic imaging, satellite imaging, industrial tasks, etc. While prior research has primarily focused on enhancing classification tasks with rotation equivariant CNNs, their impact on more complex architectures, such as U-Net for image segmentation, remains scarcely explored. Indeed, previous work interested in integrating rotation-equivariance into U-Net architecture have focused on solving specific applications with a limited scope. In contrast, this paper aims to provide a more exhaustive evaluation of rotation equivariant U-Net for image segmentation across a broader range of tasks. We benchmark their effectiveness against standard U-Net architectures, assessing improvements in terms of performance and sustainability (i.e., computational cost). Our evaluation focuses on datasets whose orientation of objects of interest is arbitrary in the image (e.g., Kvasir-SEG), but also on more standard segmentation datasets (such as COCO-Stuff) as to explore the wider applicability of rotation equivariance beyond tasks undoubtedly concerned by rotation equivariance. The main contribution of this work is to provide insights into the trade-offs and advantages of integrating rotation equivariance for segmentation tasks.