Automatic detection and measurement of femur length from fetal ultrasonography

Mukherjee, Prateep; Swamy, Gokul; Gupta, Madhumita; Patil, Uday; Krishnan, Kasiraman

doi:10.1117/12.844340

Cited by 12 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One study assessed automated analysis of fetal brain morphology on standard cranial ultrasound sections to estimate the gestational age in second and third trimester fetuses, compared with standard fetal biometry using a CNN with supervised learning based on previously labeled images 58 . Five studies assessed deep learning for automated measurement of the FL only 59–63 and two studies assessed automated measurement of AC only 64,65 . There were four studies assessing correct detection of the fetal abdominal standard plane (FASP) using AI 66–69 and five studies assessed various combinations of two or more fetal biometry measurements 70–74 …”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in obstetric ultrasound: A scoping review

2023

View full text Add to dashboard Cite

The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in obstetric ultrasound: A scoping review

2023

View full text Add to dashboard Cite

show abstract

“…Traditional Image Processing methods (Chakkarwar, Joshi & Revankar, 2010;Wee et al, 2010;Rawat et al, 2013;Ibrahim et al,2017;Lu et al, 2005;Satwika et al, 2013;Foi et al, 2014;Sahili et al, 2019;Rasheed et al, 2021;Thomas, Peters & Jeanty, 1991;Mukherjee et al, 2010;Wang, 2014;Methews & Deepa, 2014;Khan et al, 2015;Yu, Wang & Chen, 2015;Amoah, Anto & Crimi, 2015;Ravishankar, Prabhu & Vaidya, 2016;Hermawati et al, 2019;Yu et al, 2008;Wang et al, 2014;Jang et al, 2018;Kim et al, 2018;Nirmala & Palanisami, 2010;Lakshmi et al, 2008;Karl, Kagan & Chaoui, 2012;Sonia & Shanti, 2015;Deng et al, 2012;Anzalone et al, 2013;Sonia & Shanti, 2016;Nie et al, 2017) Supervised Learning methods (Khazendar et al, 2014;Ibrahim et al, 2017;Lu et al, 2005;Sahli et al, 2019;Carneiro et al, 2008;Anjit & Rishidas, 2011;Rafeek & Gunasundari, 2013;Deng et al, 2012;Park et al, 2013;Sciortino, Tegolo & Valenti, 2017) Unsupervised Learning methods…”

Section: Classification Of Existing Studies According To the Techniqu...mentioning

confidence: 99%

“…It was noted by the authors that automatic measurement of the gestational sac was successfully performed with the presented method. Mukherjee et al (2010) used polynomial curve fitting technique for automatic FL detection and measurement. Otsu threshold and curvature-based thresholding procedures were used to differentiate the femur, from other regions.…”

Section: Traditional Image Processingmentioning

confidence: 99%

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Ataş¹,

Yılmaz²

2022

acin

View full text Add to dashboard Cite

Computer aided diagnostic methods have been helping medical experts for monitoring fetus health for many years. The use of new methods has made a positive effect in monitoring the health of fetus as well as the diagnosis of anomalies. This study first introduces the indicators for identifying anomalies in fetus and gives basic information about computer-based methods such as traditional image processing, machine learning and deep learning. Then an overview of existing studies which use novel techniques on monitoring fetus health and anomaly detection from ultrasound images is given. Finally, the main challenges of novel techniques and future directions of research on computer-aided monitoring of fetus health are summarized.

show abstract

“…For the fetal movement tracing acquired by actography, Ostu method [17,18] is used to set a threshold automatically to distinguish the fetal movement parts from background noises. The parts above threshold are considered as fetal movement, and the rest are background noises.…”

Section: Fetal Movement Recognition From Actographymentioning

confidence: 99%

Fetal heart rate baseline estimation with analysis of fetal movement signal

Wei

2014

Bio-Medical Materials and Engineering

View full text Add to dashboard Cite

In order to assign appropriate baseline estimation algorithms to different fetal heart rate tracing, a method to evaluate the fetal heart rate (FHR) baseline combining with the fetal movement information was proposed. Fetal actography and tocography were used to extract the fetal movement information. The results showed that the combined method, where the fetal movement detection result was the union of results of actography and tocography, achieved a better performance with the highest sensitivity and an acceptable positive predictive value (PPV). Furthermore, the mean absolute errors (MAEs) of basal FHR values between the two algorithms and the expert were calculated with respect to the duration coefficient of fetal movement. The results showed that the algorithm using empirical mode decomposition (EMD) and Kohonen neural network (KNN) had lower MAEs than a traditional linear baseline estimation algorithm as the duration coefficient increased. However, if the duration coefficient is below 0.2, the errors may be tolerant for the FHR baseline estimation by a linear baseline estimation algorithm, which indicates that different algorithms may be selected for FHR baseline estimation based on different duration coefficients of fetal movement.

show abstract

Automatic detection and measurement of femur length from fetal ultrasonography

Cited by 12 publications

References 0 publications

Artificial intelligence in obstetric ultrasound: A scoping review

Artificial intelligence in obstetric ultrasound: A scoping review

Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review

Fetal heart rate baseline estimation with analysis of fetal movement signal

Contact Info

Product

Resources

About