Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images

2020 ◽  
Vol 58 (11) ◽  
pp. 2879-2892
Author(s):  
Peixuan Li ◽  
Huaici Zhao ◽  
Pengfei Liu ◽  
Feidao Cao
Keyword(s):  
Ultrasound Images ◽  
Automated Measurement ◽  
Fetal Head ◽  
Parameter Modification

scholarly journals Automated measurement of fetal head circumference using 2D ultrasound images

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0200412 ◽  
Author(s):  
Thomas L. A. van den Heuvel ◽  
Dagmar de Bruijn ◽  
Chris L. de Korte ◽  
Bram van Ginneken
Keyword(s):  
Head Circumference ◽  
Ultrasound Images ◽  
Automated Measurement ◽  
Fetal Head ◽  
2D Ultrasound

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.

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

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
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