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

Snawar Hussain1, Fan Guo1, Weiqing Li1

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Computer Methods and Programs in Biomedicine
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Summary

This study introduces an improved U-net architecture for retinal blood vessel segmentation, enhancing accuracy and robustness for early disease detection. The method demonstrates superior performance over existing techniques, aiding in the prevention of vision impairments.

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Area of Science:

  • Medical imaging analysis
  • Computer vision
  • Ophthalmology

Background:

  • Retinal image segmentation is crucial for detecting pathological disorders by analyzing retinal blood vessels.
  • Early detection of vascular changes can prevent blindness and vision impairments.
  • Existing segmentation methods require improved sensitivity and robustness.

Purpose of the Study:

  • To propose an automatic retinal blood vessel segmentation method using an enhanced U-net architecture.
  • To improve the accuracy and robustness of blood vessel segmentation for better clinical detection of eye diseases.

Main Methods:

  • Developed an end-to-end U-net based framework incorporating preprocessing and data augmentation.
  • Implemented multiscale input and multioutput modules with improved skip connections.
  • Utilized dilated convolutions with varying rates for effective feature extraction.

Main Results:

  • Achieved high accuracy (0.9680-0.9701) and Intersection over Union (0.7951-0.8698) on DRIVE, STARE, and CHASE datasets.
  • Demonstrated superior sensitivity (0.8263-0.8837) compared to baseline methods.
  • Ablation studies confirmed the contribution of each proposed module.

Conclusions:

  • The proposed U-net based method outperforms original U-net and other state-of-the-art segmentation techniques.
  • The enhanced architecture shows robustness to noise in retinal images.
  • This method offers a more effective tool for automated retinal blood vessel segmentation.