Segmentation of fetal envelope from 3D ultrasound images based on pixel intensity statistical distribution and shape priors

2013 ◽  
Author(s):  
Sonia Dahdouh ◽  
Antoine Serrurier ◽  
Gilles Grange ◽  
Elsa D. Angelini ◽  
Isabelle Bloch
Keyword(s):  
3D Ultrasound ◽  
Statistical Distribution ◽  
Ultrasound Images ◽  
Shape Priors ◽  
Pixel Intensity

scholarly journals Segmentation of embryonic and fetal 3D ultrasound images based on pixel intensity distributions and shape priors

2015 ◽  
Vol 24 (1) ◽  
pp. 255-268 ◽  
Author(s):  
Sonia Dahdouh ◽  
Elsa D. Angelini ◽  
Gilles Grangé ◽  
Isabelle Bloch
Keyword(s):  
3D Ultrasound ◽  
Ultrasound Images ◽  
Shape Priors ◽  
Pixel Intensity

scholarly journals Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristi Powers ◽  
Raymond Chang ◽  
Justin Torello ◽  
Rhonda Silva ◽  
Yannick Cadoret ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Analysis ◽  
Structure And Function ◽  
Ultrasound Images ◽  
Cardiac Structure ◽  
Short Axis ◽  
Pixel Intensity ◽  
Cardiac Structure And Function ◽  
And Function

AbstractEchocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.

Prostate boundary segmentation from 3D ultrasound images

2003 ◽  
Vol 30 (7) ◽  
pp. 1648-1659 ◽  
Author(s):  
Ning Hu ◽  
Dónal B. Downey ◽  
Aaron Fenster ◽  
Hanif M. Ladak
Keyword(s):  
3D Ultrasound ◽  
Ultrasound Images

Public database for validation of follicle detection algorithms on 3D ultrasound images of ovaries

2020 ◽  
Vol 196 ◽  
pp. 105621
Author(s):  
Božidar Potočnik ◽  
Jurij Munda ◽  
Milan Reljič ◽  
Ksenija Rakić ◽  
Jure Knez ◽  
...  
Keyword(s):  
3D Ultrasound ◽  
Ultrasound Images ◽  
Public Database ◽  
Detection Algorithms
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