DilUnet: A U-net based architecture for blood vessels segmentation.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Retinal image segmentation can help clinicians detect pathological disorders by studying changes in retinal blood vessels. This early detection can help prevent blindness and many other vision impairments. So far, several supervised and unsupervised methods have been proposed for the task of automatic blood vessel segmentation. However, the sensitivity and the robustness of these methods can be improved by correctly classifying more vessel pixels.

Authors

  • Snawar Hussain
    School of Automation, Central South University, Changsha, Hunan 410083, China.
  • Fan Guo
    Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
  • Weiqing Li
    School of Automation, Central South University, Changsha, Hunan 410083, China.
  • Ziqi Shen
    School of Automation, Central South University, Changsha, Hunan 410083, China.