Basic as well as detailed neurosonograms can be performed by offline analysis of three-dimensional fetal brain volumes

2009 ◽  
Vol 36 (1) ◽  
pp. 20-25 ◽  
Author(s):  
E. Bornstein ◽  
A. Monteagudo ◽  
R. Santos ◽  
I. Strock ◽  
T. Tsymbal ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Fetal Brain ◽  
Brain Volumes ◽  
Offline Analysis

Evaluation of offline analysis of archived three-dimensional volume datasets in the diagnosis of fetal brain abnormalities

2011 ◽  
Vol 38 (2) ◽  
pp. 165-169 ◽  
Author(s):  
M. M. Salman ◽  
P. Twining ◽  
H. Mousa ◽  
D. James ◽  
M. Momtaz ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Fetal Brain ◽  
Brain Abnormalities ◽  
Offline Analysis

scholarly journals OP25.01: How accurate is the offline analysis of 3D volume datasets in the diagnosis of fetal brain abnormalities?

2010 ◽  
Vol 36 (S1) ◽  
pp. 124-124
Author(s):  
M. Salman ◽  
H. Mousa ◽  
P. Twining ◽  
D. K. James ◽  
M. Momtaz ◽  
...  
Keyword(s):  
Fetal Brain ◽  
Brain Abnormalities ◽  
Offline Analysis ◽  
3D Volume

scholarly journals Quantitative analysis of normal fetal brain volume and flow by three-dimensional power Doppler ultrasound

2013 ◽  
Vol 76 (9) ◽  
pp. 504-509 ◽  
Author(s):  
Ju-Chun Hsu ◽  
Yi-Cheng Wu ◽  
Peng-Hui Wang ◽  
Hsing-I Wang ◽  
Chi-Mou Juang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Doppler Ultrasound ◽  
Brain Volume ◽  
Power Doppler ◽  
Three Dimensional ◽  
Fetal Brain ◽  
Power Doppler Ultrasound

3D volume reconstruction of the fetal brain in twin-twin transfusion syndrome: brain volumes are concordant in ex-recipients and ex-donors

2012 ◽  
Vol 97 (Suppl 1) ◽  
pp. A9.1-A9
Author(s):  
M Taylor-Clarke ◽  
V Kyriakopoulou ◽  
J Allsop ◽  
RC Wimalasundera ◽  
HM Gardiner ◽  
...  
Keyword(s):  
Fetal Brain ◽  
Volume Reconstruction ◽  
Brain Volumes ◽  
3D Volume

scholarly journals 4252 Automated Fetal Brain Volumetry on Clinical Fetal MRI Using Convolutional Neural Network

2020 ◽  
Vol 4 (s1) ◽  
pp. 45-46
Author(s):  
Carol Tran ◽  
Orit Glenn ◽  
Christopher Hess ◽  
Andreas Rauschecker
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Volume ◽  
Brain Mri ◽  
Fetal Brain ◽  
Fetal Mri ◽  
Total Brain Volume ◽  
Brain Volumes

OBJECTIVES/GOALS: We seek to develop an automated deep learning-based method for segmentation and volumetric quantification of the fetal brain on T2-weighted fetal MRIs. We will evaluate the performance of the algorithm by comparing it to gold standard manual segmentations. The method will be used to create a normative sample of brain volumes across gestational ages. METHODS/STUDY POPULATION: We will adapt a U-Net convolutional neural network architecture for fetal brain MRIs using 3D volumes. After re-sampling 2D fetal brain acquisitions to 3mm3 3D volumes using linear interpolation, the network will be trained to perform automated brain segmentation on 40 randomly-sampled, normal fetal brain MRI scans of singleton pregnancies. Training will be performed in 3 acquisition planes (axial, coronal, sagittal). Performance will be evaluated on 10 test MRIs (in 3 acquisition planes, 30 total test samples) using Dice scores, compared to radiologists’ manual segmentations. The algorithm’s performance on measuring total brain volume will also be evaluated. RESULTS/ANTICIPATED RESULTS: Based on the success of prior U-net architectures for volumetric segmentation tasks in medical imaging (e.g. Duong et al., 2019), we anticipate that the convolutional neural network will accurately provide segmentations and associated volumetry of fetal brains in fractions of a second. We anticipate median Dice scores greater than 0.8 across our test sample. Once validated, the method will retrospectively generate a normative database of over 1500 fetal brain volumes across gestational ages (18 weeks to 30 weeks) collected at our institution. DISCUSSION/SIGNIFICANCE OF IMPACT: Quantitative estimates of brain volume, and deviations from normative data, would be a major advancement in objective clinical assessments of fetal MRI. Such data can currently only be obtained through laborious manual segmentations; automated deep learning methods have the potential to reduce the time and cost of this process.

Three-dimensional HDlive Thick-Slice Silhouette of Fetal Brain

2016 ◽  
Vol 10 (1) ◽  
pp. 1-2
Author(s):  
Ritsuko K Pooh
Keyword(s):  
Obstet Gynecol ◽  
Three Dimensional ◽  
Fetal Brain ◽  
Normal Brain ◽  
Fetal Anomalies ◽  
High Definition ◽  
Brain Image ◽  
Great Achievement ◽  
4D Ultrasound

ABSTRACT Three-dimensional (3D) and four-dimensional (4D) ultrasound have improved our knowledge regarding the development of the embryo and fetus and of a great number of fetal anomalies. The great achievement in the field of 3D/4D ultrasound is high definition live (HDlive) technology and HDlive silhouette/flow technology. HDlive silhouette emphasizes the borderlines between organs with different echogenicity and it can be appropriately named as ‘see-through fashion’. However, it occasionally appears to demonstrate too many inner structures overlapping one another to understand their relations. The author has cut the volume dataset with a rectangle cube and rendered the cut slice with silhouette ultrasound and called as ‘thick-slice silhouette’. Normal brain image in the coronal cutting section by thick-slice silhouette imaging is the picture of the month. This method is useful to identify the inner structure of the organs. How to cite this article Pooh RK. Three-dimensional HDlive Thick-Slice Silhouette of Fetal Brain. Donald School J Ultrasound Obstet Gynecol 2016;10(1):1-2.

scholarly journals OP03.11: Evaluation of fetal brain malformations by three-dimensional ultrasonography and magnetic resonance imaging

2011 ◽  
Vol 38 (S1) ◽  
pp. 65-66
Author(s):  
W. Hisaba ◽  
P. Soares ◽  
P. M. Nowak ◽  
H. Figuinha ◽  
T. Kawanami ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Three Dimensional ◽  
Fetal Brain ◽  
Resonance Imaging ◽  
Brain Malformations
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