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

Yaopeng Peng; Milan Sonka; Danny Z Chen

doi:10.48550/arxiv.2311.17791

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

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

Yaopeng Peng, Milan Sonka and Danny Z Chen

ArXiv.org

Cornell University

11/29/2023

DOI: 10.48550/arxiv.2311.17791

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https://doi.org/10.48550/arxiv.2311.17791View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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

Computer Science - Computer Vision and Pattern Recognition

Details

Title: Subtitle: U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
Creators: Yaopeng Peng
Milan Sonka
Danny Z Chen
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2311.17791
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 11/29/2023
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984543190702771

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