Strided U-Net model: Retinal vessels segmentation using dice loss

Toufique A. Soomro, Olaf Hellwich, Ahmed J. Afifi, Junbin Gao, Manoranjan Paul, Lihong Zheng

Research output: Book chapter/Published conference paperConference paper

8 Citations (Scopus)

Abstract

Accurate segmentation of vessels is an arduous task in the analysis of medical images, particularly the extraction of vessels from colored retinal fundus images. Many image processing tactics have been implemented for accurate detection of vessels, but many vessels have been dropped. In this paper, we propose a deep learning method based on the convolutional neural network (CNN) with dice loss function for retinal vessel segmentation. To our knowledge, we are the first to form the CNN on the basis of the dice loss function for the extraction of vessels from a colored retinal image. The pre-processing steps are used to eliminate uneven illumination to make the training process more efficient. We implement the CNN model based on a variational auto-encoder (VAE), which is a modified version of U-Net. Our main contribution to the implementation of CNN is to replace all pooling layers with progressive convolution and deeper layers. It takes the retinal image as input and generates the image of segmented output vessels with the same resolution as the input image. The proposed segmentation method showed better performance than the existing methods on the most used databases, namely: DRIVE and STARE. In addition, it gives a sensitivity of 0.739 on the DRIVE database with an accuracy of 0.948 and a sensitivity of 0.748 on the STARE database with an accuracy of 0.947.

Original languageEnglish
Title of host publication2018 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2018
EditorsMark Pickering, Lihong Zheng, Shaodi You, Ashfaqur Rahman, Manzur Murshed, Md Asikuzzaman, Ambarish Natu, Antonio Robles-Kelly, Manoranjan Paul
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781538666029
DOIs
Publication statusPublished - 16 Jan 2019
Event2018 International Conference on Digital Image Computing: Techniques and Applications: DICTA 2018 - Canberra Rex Hotel, Canberra, Australia
Duration: 10 Dec 201813 Dec 2018
https://dicta2018.org/ (Conference website)

Publication series

Name2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Conference

Conference2018 International Conference on Digital Image Computing: Techniques and Applications
CountryAustralia
CityCanberra
Period10/12/1813/12/18
Internet address

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  • Cite this

    Soomro, T. A., Hellwich, O., Afifi, A. J., Gao, J., Paul, M., & Zheng, L. (2019). Strided U-Net model: Retinal vessels segmentation using dice loss. In M. Pickering, L. Zheng, S. You, A. Rahman, M. Murshed, M. Asikuzzaman, A. Natu, A. Robles-Kelly, & M. Paul (Eds.), 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018 [8615770] (2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2018.8615770