Automatic detection and measurement of fetal femur length using a portable ultrasound device

Khan, Naiad Hossain; Tegnander, Eva; Dreier, Johan Morten; Eik‐Nes, S. H.; Torp, Hans; Kiss, Gabriel

doi:10.1109/ultsym.2015.0486

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…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%

“…In the study, grayscale, smoothing, dilatation, erosion and binary threshold were used respectively in the pre-processing step, and then the Canny edge detection function of the OpenCV library was used to find edge and measure BPD. Khan et al (2015) developed an automated method that can work on a tablet device to detect and measure FL.…”

Section: Traditional Image Processingmentioning

confidence: 99%

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

Ataş¹,

Yılmaz²

2022

acin

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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.

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

confidence: 99%

Section: Traditional Image Processingmentioning

confidence: 99%

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

Ataş¹,

Yılmaz²

2022

acin

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“…Pengukuran biometri dilakukan untuk memperkirakan kelainan pertumbuhan janin serta estimasi berat janin dan Gestational Age (GA). Estimasi berat janin dan GA yang akurat penting untuk memberikan perlakuan optimal selama masa kehamilan [2]. Pengukuran biometri janin meliputi pengukuran Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC) dan Femur Length (FL) [3].…”

Section: Pendahuluanunclassified

Deteksi Otomatis Bidang Kepala Janin dari Citra Ultrasonografi 2 Dimensi

Aji¹,

Sardjono²,

Fatoni³

2020

JTITS

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Modalitas pencitraan primer untuk untuk pemeriksaan anatomi dan fisiologi janin adalah alat ultrasonografi (USG) medis 2 dimensi mengingat harganya yang murah, ketersediaan yang melimpah, kemampuan realtime, dan tidak adanya bahaya radiasi. Head Circumference (HC) merupakan parameter pengukuran biometri janin yang dianalisa untuk mengetahui perkembangan janin secara kuantitatif dengan menggunakan mesin USG. Pada praktik klinis, karena rasio signal-to-noise yang rendah, klinisi seringkali mengalami kesulitan untuk mengenali bidang janin dengan tepat. Lebih dari itu, klinisi kesulitan untuk membuat elips yang paling mendekati hanya dengan tiga titik parameter minor dan mayor yang disediakan mesin USG. Proses deteksi dan pengukuran HC secara manual oleh klinisi membutuhkan waktu yang cukup lama dan akurasinya sangat bergantung dengan pengalaman dan kemampuan klinisi. Penelitian mengenai deteksi dan pengukuran otomatis HC sedang menjadi bidang penelitian yang cukup aktif. Dalam penelitian ini diajukan sebuah sistem deteksi otomatis untuk HC. Metode Convolutional Neural Network (CNN) diajukan untuk melakukan klasifikasi bidang elips janin dari jaringan ibu maupun jaringan janin lainnya. CNN dipekerjakan untuk pixel-wise classification citra ke dalam kelas bidang janin, maternal tissue, ataupun background. Pada penelitian ini, dari 13 citra uji, didapatkan rata rata akurasi segmentasi semantik sebesar 0.76.

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“…Furthermore, continuous movement of the probe may irritate the mother that can lead to undesirable movement, which in turn, enhances the resultant error [8]. It should also be noted that the availability of skilled sonographers poses a great challenge [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Automated Fetal Head Classification and Segmentation Using Ultrasound Video

et al. 2021

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During pregnancy, fetal ultrasound provides essential insight into a baby's growth and development. In this ultrasound, accurate assessment of fetal head biometry is critical to the clinical management of pregnancy. Current methodologies used for fetal head biometry heavily rely upon sonographer skills and experience to locate a baby's head. In this paper a novel approach is proposed to automate the fetal head biometry using live ultrasound feed; which is also capable of tackling low abdominal contrast against surroundings. Proposed model is trained on ALEXNET and UNET for classification and segmentation of headframes respectively from ultrasound video. To compute biparietal diameter (BPD) and head circumference (HC); which are essential requirements to compute gestational age, an ellipse is drawn on the contour of the annotated segmented fetal head. It should be noted that to validate gestational age estimate, ellipse are drawn on multiple best classified headframes obtained using ALEXNET. The proposed system is able to estimate gestational age within clinically acceptable ± one week of observed gestational age with an accuracy of 96%. Moreover, it uses robust machine vision features to reduce the sonographer's interaction with the system, thus reducing the overall procedure time and making it independent of sonographer's skill.

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Automatic detection and measurement of fetal femur length using a portable ultrasound device

Cited by 10 publications

References 8 publications

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

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

Deteksi Otomatis Bidang Kepala Janin dari Citra Ultrasonografi 2 Dimensi

Automated Fetal Head Classification and Segmentation Using Ultrasound Video

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