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Related Experiment Video

Updated: Nov 19, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning.

Bo Zhang¹, Han Liu², Hong Luo¹

¹Department of Ultrasound, West China Second Hospital, Sichuan University/ Key Laboratory of Obstetrics & Gynecology, Pediatric Diseases, and Birth Defects of the Ministry of Education.

|February 3, 2021

Summary

This study introduces an automated system for assessing fetal sonographic (FS) image quality. The multitask learning approach efficiently identifies key anatomical structures, aiding in accurate diagnosis and reducing workload.

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Last Updated: Nov 19, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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Published on: January 7, 2021

Murine Fetal Echocardiography

Murine Fetal Echocardiography

Published on: February 15, 2013

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

Published on: April 7, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Obstetrics

Background:

Fetal sonographic (FS) image quality control is crucial for accurate biometric measurements and anomaly detection.
Current quality control methods are labor-intensive and require expert sonographers.

Purpose of the Study:

To develop an automated image quality assessment scheme for FS images using multitask learning.
To assist in the quality control of fetal sonography, improving efficiency and accuracy.

Main Methods:

A multitask learning framework employing three convolutional neural networks (CNNs).
A Feature Extraction Network extracts deep features.
A Class Prediction Network assesses structure standards, and a Region Proposal Network locates them.

Main Results:

The scheme achieves rapid quality assessment of FS images in under a second.
Demonstrates competitive performance in segmentation and diagnosis compared to existing methods.
Successfully applied to head, abdominal, and heart fetal sections.

Conclusions:

The proposed automated scheme offers an efficient solution for FS image quality control.
This AI-driven approach can support sonographers in achieving higher diagnostic accuracy.
Enables faster and more reliable assessment of essential fetal anatomical structures.