Recurrent Residual U-Net for Medical Image Segmentation

Author(s)

Md Zahangir Alom, University of DaytonFollow
Christopher Yakopcic, University of DaytonFollow
Mahmudul Hasan, Comsat Labs
Tarek M. Taha, University of DaytonFollow
Vijayan K. Asari, University of DaytonFollow

Document Type

Article

Publication Date

1-2019

Publication Source

Journal of Medical Imaging

Abstract

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architectures with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U-Net. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

ISBN/ISSN

2329-4302

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1117/1.JMI.6.1.014006

Publisher

SPIE - Soc Photo-Optical Instrumentation Engineers

Volume

6

Peer Reviewed

yes

Issue

1

Sponsoring Agency

National Science Foundation (NSF) ; NSF - Directorate for Computer & Information Science & Engineering (CISE) ; NSF - Directorate for Engineering (ENG) ; NSF - Division of Electrical, Communications & Cyber Systems (ECCS)

eCommons Citation

Alom, Md Zahangir; Yakopcic, Christopher; Hasan, Mahmudul; Taha, Tarek M.; and Asari, Vijayan K., "Recurrent Residual U-Net for Medical Image Segmentation" (2019). Electrical and Computer Engineering Faculty Publications. 447.
https://ecommons.udayton.edu/ece_fac_pub/447