Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images

Kim, Bukweon; Kim, Kang Cheol; Park, Yejin; Kwon, Ja Young; Jang, Jaeseong; Seo, Jin Keun

doi:10.1088/1361-6579/aae255

Cited by 50 publications

(40 citation statements)

References 26 publications

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“…Lorenz et al [26] recently published a pipeline combining RF, shape models, and CNN to automatically perform view recognition and anatomical landmark location, with the objective of measuring the AC from 3-D US recordings. Similarly, Kim et al [27] used a CNN to estimate AC from 2-D US data. For further information on biometric measurements, we refer the reader to a recent review of automated techniques for the interpretation of fetal abnormalities [28].…”

Section: For Image Quantification and Feature Extractionmentioning

confidence: 99%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

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In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this re-view we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

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Section: For Image Quantification and Feature Extractionmentioning

confidence: 99%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

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“…Kim et al [20] proposed the ML-based Automatic identification of fetal abdominal circumference (AIFAC) from images of ultrasound. This paper introduced a method for the automatic estimation of fetal Biometry from 2D ultrasound data through multiple processes consisting of a specially designed neural convolution (CNN) and a U-Net network for each process.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using Logistic Regression Classifier With an Advanced Machine Learning Approach

Zhang

Han

2020

IEEE Access

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This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The stateof-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates. INDEX TERMS Machine learning, convolution neural network, logistic regression model, obstetric ultrasound, IoMT. I. BACKGROUND AND INTRODUCTION

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“…A discriminative constrained probabilistic boosting tree (DCPBT) classifier for measuring and identifying the abnormality of the fetus regarding head, abdominal structure, and femur [12][13][14] used the Adaboost method for object detection in 2D fetal abdominal ultrasound images. Kim et al (2018) 15 used an automatic method for estimating fetal biometrics in ultrasound images. Automation is obtained from machine learning algorithms, but it cannot provide automatic feature extraction.…”

Section: Background Studymentioning

confidence: 99%

“…From the obtained results given in Table 6 The segmentation of image components and classification accuracy are calculated separately and compared with the existing approach as FUIQA-E1 [24] and existing approach. 15 The performance parameters are calculated for segmentation carried out separately on each component and compared with one another. The comparison results in terms of sensitivity, specificity, and accuracy for segmentation is given in Table 7.…”

Section: F I G U R E 13 Image Segmentation and Classification Using Cmentioning

confidence: 99%

Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images

Deepika¹,

Suresh²,

Pabitha

2020

Computational Intelligence

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One of the most important industries which protect human from various diseases is the medical industry. Child death is a crucial concern that needs to concentrate on "save the children." Abnormality of a child can be obtained by diagnosing the prenatal by ultrasound system within a specific period for providing better treatment to do "save the children.". This article aimed to diagnose the (prenatal) ultrasound-images by design and implement a novel framework named Defending Against Child Death (DACD). The existing method is a semiautomatic method where it used convolutional neural network (CNN) algorithm for classifying ultrasound images. Real-time medical industry requires a fully automatic method for classifying the ultrasound images to save the human. Hence this article, includes deep learning by implementing five convolutional neural network architectures in an order where it learns, estimate, and confirms the fetus parameters. All the layers in the convolutional neural network extract and classify the different number of features in the ultrasound images automatically and provide a result. The increased number of hidden layers in the CNN can extract even the hidden features of the images. The extracted features are classified automatically and improve the accuracy of 128

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Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images

Abstract: Our method achieved a Dice similarity metric of [Formula: see text] for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.

Cited by 50 publications

References 26 publications

Machine Learning in Fetal Cardiology: What to Expect

Machine Learning in Fetal Cardiology: What to Expect

Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using Logistic Regression Classifier With an Advanced Machine Learning Approach

Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images

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