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Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets.

Le Nhi Lam Thuy1,2, Tan Dat Trinh1, Le Hoang Anh1

  • 1Information Science Faculty, Sai Gon University, Vietnam.

Biomed Research International
|April 26, 2021
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Summary

This study introduces a novel coarse-to-fine method for segmenting coronary arteries of varying sizes in angiograms. The approach effectively extracts both primary and secondary vessels, improving coronary artery segmentation accuracy.

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Area of Science:

  • Medical Imaging
  • Cardiovascular Imaging
  • Image Segmentation

Background:

  • Coronary arteries vary significantly in size and visual contrast within angiograms.
  • Segmenting all coronary artery sizes with a single model is challenging due to differing properties and fragmented vessels.

Purpose of the Study:

  • To develop a novel, coarse-to-fine method for accurate coronary artery extraction from angiograms.
  • To address the limitations of single-model segmentation for diverse coronary artery sizes.

Main Methods:

  • A U-net model was employed for initial segmentation of the main coronary artery.
  • A new algorithm identified junctions between primary and secondary coronary arteries.
  • A second U-net model segmented secondary coronary arteries within defined regions.

Main Results:

  • The proposed method achieved a Dice coefficient of 76.40% on coronary X-ray datasets.
  • The coarse-to-fine strategy demonstrated effectiveness in segmenting varied coronary artery structures.

Conclusions:

  • The novel segmentation approach shows significant potential for improving coronary vessel segmentation in medical imaging.
  • This method offers a promising solution for analyzing coronary arteries of different scales.