[Left ventricle segmentation in echocardiography based on adaptive mean shift].

Area of Science:

• Medical imaging analysis
• Computational cardiology
• Artificial intelligence in healthcare

Context:

• Echocardiography is crucial for assessing cardiac function via ventricular volume parameters.
• Manual segmentation of ultrasound images is labor-intensive and prone to errors due to noise and complexity.
• Existing automatic segmentation methods often lack sufficient accuracy.

Purpose:

• To develop a robust and accurate automated framework for ventricle segmentation in echocardiography.
• To overcome the limitations of manual segmentation and improve the reliability of automatic methods.
• To enhance the evaluation of cardiac function through precise ventricular volume measurements.

Summary:

• A novel algorithm framework combines Faster R-CNN for region of interest localization, K-means for pre-segmentation, and an adaptive mean shift algorithm for refined segmentation.
• Region growing is employed as the final step to delineate the ventricle automatically.
• The adaptive mean shift algorithm demonstrates superior stability and accuracy compared to fixed-bandwidth approaches.

Impact:

• Enables automatic, accurate ventricle segmentation in echocardiography, reducing manual workload.
• Improves the precision of cardiac function assessment, aiding clinical diagnosis.
• Provides a more stable and reliable method for analyzing ultrasound images of the heart.