Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning

by   Hong Luo, et al.

The quality control of fetal sonographic (FS) images is essential for the correct biometric measurements and fetal anomaly diagnosis. However, quality control requires professional sonographers to perform and is often labor-intensive. To solve this problem, we propose an automatic image quality assessment scheme based on multi-task learning to assist in FS image quality control. An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary. Therefore, our scheme aims to identify those essential anatomical structures to judge whether an FS image is the standard image, which is achieved by three convolutional neural networks. The Feature Extraction Network aims to extract deep level features of FS images. Based on the extracted features, the Class Prediction Network determines whether the structure meets the standard and Region Proposal Network identifies its position. The scheme has been applied to three types of fetal sections, which are the head, abdominal, and heart. The experimental results show that our method can make a quality assessment of an FS image within less a second. Also, our method achieves competitive performance in both the detection and classification compared with state-of-the-art methods.


page 1

page 3

page 4

page 5

page 11


No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

Blind or no-reference image quality assessment (NR-IQA) is a fundamental...

Echocardiographic Image Quality Assessment Using Deep Neural Networks

Echocardiography image quality assessment is not a trivial issue in tran...

Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics

Omnidirectional image quality assessment (OIQA) aims to predict the perc...

A Deep Retinal Image Quality Assessment Network with Salient Structure Priors

Retinal image quality assessment is an essential prerequisite for diagno...

Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos

Smartphone is the most successful consumer electronic product in today's...

PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment

Blind image quality assessment (BIQA) remains challenging due to the div...

RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

Coronary CT Angiography (CCTA) is susceptible to various distortions (e....

Please sign up or login with your details

Forgot password? Click here to reset