Quality Assessment of Fetal Head Ultrasound Images Based on Faster R-CNN

2018 ◽  
pp. 38-46 ◽  
Author(s):  
Zehui Lin ◽  
Minh Hung Le ◽  
Dong Ni ◽  
Siping Chen ◽  
Shengli Li ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Ultrasound Images ◽  
Fetal Head

Multi-task learning for quality assessment of fetal head ultrasound images

2019 ◽  
Vol 58 ◽  
pp. 101548 ◽  
Author(s):  
Zehui Lin ◽  
Shengli Li ◽  
Dong Ni ◽  
Yimei Liao ◽  
Huaxuan Wen ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Ultrasound Images ◽  
Fetal Head ◽  
Task Learning

scholarly journals Mask-R$$^{2}$$CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images

2021 ◽  
Author(s):  
Sara Moccia ◽  
Maria Chiara Fiorentino ◽  
Emanuele Frontoni
Keyword(s):  
Fetal Growth ◽  
A Priori ◽  
Absolute Difference ◽  
Ultrasound Images ◽  
Fetal Head ◽  
Mean Absolute Difference ◽  
Distance Field ◽  
Feature Pyramid ◽  
End To End ◽  
Ablation Study

Abstract Background and objectives Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R$$^{2}$$ 2 CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods Mask-R$$^{2}$$ 2 CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results Mask-R$$^{2}$$ 2 CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R$$^{2}$$ 2 CNN achieved a mean absolute difference of 1.95 mm (standard deviation $$=\pm 1.92$$ = ± 1.92  mm), outperforming other approaches in the literature. Conclusions With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R$$^{2}$$ 2 CNN may be an effective support for clinicians for assessing fetal growth.

scholarly journals Texture Based Quality Analysis of Simulated Synthetic Ultrasound Images Using Local Binary Patterns

2017 ◽  
Author(s):  
Prerna Singh ◽  
Ramakrishnan Mukundan ◽  
Rex De Ryke
Keyword(s):  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Ground Truth ◽  
Speckle Noise ◽  
Local Binary Patterns ◽  
Quality Analysis ◽  
Ultrasound Images ◽  
Ground Truth Data

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

scholarly journals Quality Assessment of Transperineal Ultrasound Images of the Male Pelvic Region Using Deep Learning

2018 ◽  
Author(s):  
Saskia Camps ◽  
Tim Houben ◽  
Christopher Edwards ◽  
Maria Antico ◽  
Matteo Dunnhofer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Quality Assessment ◽  
Transperineal Ultrasound ◽  
Ultrasound Images ◽  
Pelvic Region

Interactive automatic fetal head measurements from ultrasound images using multimedia computer technology

1997 ◽  
Vol 23 (5) ◽  
pp. 665-673 ◽  
Author(s):  
Sayan Dev Pathak ◽  
Vikram Chalana ◽  
Yongmin Kim
Keyword(s):  
Computer Technology ◽  
Ultrasound Images ◽  
Fetal Head

scholarly journals Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image

2017 ◽  
Vol 4 (2) ◽  
pp. 024001 ◽  
Author(s):  
Lei Zhang ◽  
Nicholas J. Dudley ◽  
Tryphon Lambrou ◽  
Nigel Allinson ◽  
Xujiong Ye
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Ultrasound Image ◽  
Two Dimensional ◽  
Fetal Head
Sign in / Sign up

Export Citation Format

Share Document