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Related Experiment Video

Updated: May 11, 2026

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

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Published on: June 3, 2018

Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images.

Soichiro Inomata¹, Takaaki Yoshimura^2,3,4,5, Minghui Tang^5,6

¹Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.

Journal of Cardiovascular Development and Disease

|January 24, 2025

Summary

Object detection using deep learning accurately identifies the aortic valve annulus in cardiac CT scans, outperforming segmentation. This method enhances precision for cardiovascular intervention planning.

Keywords:

aortic valve convolution neural network deep learning

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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Area of Science:

Cardiovascular Imaging
Medical Artificial Intelligence
Deep Learning Applications

Background:

Accurate delineation of the aortic valve annulus is crucial for cardiovascular interventions.
Manual segmentation of the aortic valve annulus from cardiac CT images is time-consuming and prone to variability.
Automated methods are needed to improve efficiency and accuracy in cardiac image analysis.

Purpose of the Study:

To evaluate deep learning techniques for automatic extraction and delineation of the aortic valve annulus from contrast-enhanced cardiac CT images.
To compare the accuracy of segmentation and object detection approaches for this task.

Main Methods:

Analysis of 32 contrast-enhanced cardiac CT scans.
Implementation of DeepLabv3+ for segmentation and YOLOv2 for object detection.
Dataset augmentation and five-fold cross-validation.
Evaluation using Dice Similarity Coefficient (DSC) and comparison of aortic valve annulus area estimation.

Main Results:

Object detection achieved a mean DSC of 0.809, superior to the segmentation approach (mean DSC 0.711).
Object detection demonstrated higher precision and recall with fewer false positives/negatives.
Mean error in aortic valve annulus area estimation was 2.55 mm.

Conclusions:

Object detection significantly outperforms segmentation for identifying the aortic valve annulus in cardiac CT.
Deep learning, particularly object detection, shows promise for clinical applications in cardiac imaging.
These findings support the potential of AI to improve accuracy and efficiency in preoperative planning for cardiovascular interventions.