U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

TL;DR

U-Net v2 introduces enhanced skip connections that infuse semantic information and finer details into features, significantly improving medical image segmentation accuracy while maintaining efficiency.

Contribution

The paper proposes a novel skip connection mechanism in U-Net v2 that enriches features with semantic and detailed information, improving segmentation performance.

Findings

Outperforms state-of-the-art methods on skin lesion and polyp segmentation datasets.

Maintains memory and computational efficiency.

Easily integrable into existing encoder-decoder architectures.

Abstract

In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical image segmentation. It aims to augment the infusion of semantic information into low-level features while simultaneously refining high-level features with finer details. For an input image, we begin by extracting multi-level features with a deep neural network encoder. Next, we enhance the feature map of each level by infusing semantic information from higher-level features and integrating finer details from lower-level features through Hadamard product. Our novel skip connections empower features of all the levels with enriched semantic characteristics and intricate details. The improved features are subsequently transmitted to the decoder for further processing and segmentation. Our method can be seamlessly integrated into any Encoder-Decoder network. We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency. Code is available at: https://github.com/yaoppeng/U-Net_v2