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Updated: Nov 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Le Nhi Lam Thuy^1,2, Tan Dat Trinh¹, Le Hoang Anh¹

¹Information Science Faculty, Sai Gon University, Vietnam.

Biomed Research International

|April 26, 2021

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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Last Updated: Nov 8, 2025

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

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.