Noble, J. Alison

Ultrasound image segmentation: a survey

Noble, J. A., & Boukerroui, D. (2006) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesUltrasound in Clinical Applications

Cited by 1,157

International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project

Papageorghiou, A. T., Ohuma, E. O., Altman, D. G., Todros, T., Ismail, L. C., Lambert, A., Jaffer, Y. A., Bertino, E., Gravett, M. G., Purwar, M., et al. (2014) · The Lancet

Pregnancy and preeclampsia studiesMaternal and Neonatal Healthcare

Cited by 870

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Drukker, L., Noble, J. A., & Papageorghiou, A. T. (2020) · Ultrasound in Obstetrics & Gynecology

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 240

Ultrasound‐based gestational‐age estimation in late pregnancy

Papageorghiou, A. T., Kemp, B., Stones, W., Ohuma, E. O., Kennedy, S. H., Purwar, M., Salomon, L. J., Altman, D. G., Noble, J. A., Bertino, E., et al. (2016) · Ultrasound in Obstetrics & Gynecology

Prenatal Screening and DiagnosticsPregnancy and preeclampsia studies

Cited by 179

A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography

Jacob, G., Noble, J. A., Behrenbruch, C., Kelion, A. D., & Banning, A. P. (2002) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiovascular Function and Risk Factors

Cited by 142

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning

Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A. (2018) · Medical Image Analysis

Medical Image Segmentation TechniquesFetal and Pediatric Neurological Disorders

Cited by 95

A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints

Yaqub, M., Kelly, B., Papageorghiou, A. T., & Noble, J. A. (2017) · Ultrasound in Medicine & Biology

Medical Image Segmentation TechniquesFetal and Pediatric Neurological Disorders

Cited by 67

A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use

McLaughlin, R. A., Hipwell, J., Hawkes, D. J., Noble, J. A., Byrne, J. V., & Cox, T. C. (2005) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 63

Machine learning for accurate estimation of fetal gestational age based on ultrasound images

Lee, L. H., Bradburn, E., Craik, R., Yaqub, M., Norris, S. A., Ismail, L. C., Ohuma, E. O., Barros, F. C., Lambert, A., Carvalho, M., et al. (2023) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationPrenatal Screening and Diagnostics

Cited by 59

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video

Drukker, L., Sharma, H., Droste, R., Alsharid, M., Chatelain, P., Noble, J. A., & Papageorghiou, A. T. (2021) · Scientific Reports

Ultrasound in Clinical ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 59

Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks

Sundaresan, V., Bridge, C. P., Ioannou, C., & Noble, J. A. (2017) · 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 58

Guided Random Forests for Identification of Key Fetal Anatomy and Image Categorization in Ultrasound Scans

Yaqub, M., Kelly, B., Papageorghiou, A. T., & Noble, J. A. (2015) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesImage Retrieval and Classification Techniques

Cited by 56

Self-Supervised Representation Learning for Ultrasound Video

Jiao, J., Droste, R., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

Domain Adaptation and Few-Shot LearningMedical Image Segmentation Techniques

Cited by 56

Measurement of spiral artery jets: general principles and differences observed in small-for-gestational-age pregnancies

Collins, S. L., Birks, J. S., Stevenson, G. N., Papageorghiou, A. T., Noble, J. A., & Impey, L. (2012) · Ultrasound in Obstetrics & Gynecology

Pregnancy and preeclampsia studiesReproductive System and Pregnancy

Cited by 55

Influence of power Doppler gain setting on Virtual Organ Computer‐aided AnaLysis indices in vivo: can use of the individual sub‐noise gain level optimize information?

Collins, S. L., Stevenson, G. N., Noble, J. A., Impey, L., & Welsh, A. W. (2012) · Ultrasound in Obstetrics & Gynecology

Ultrasound in Clinical ApplicationsPregnancy and preeclampsia studies

Cited by 52

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos

Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A. (2021) · Medical Image Analysis

Surgical Simulation and TrainingArtificial Intelligence in Healthcare and Education

Cited by 48

3-D Ultrasound Segmentation of the Placenta Using the Random Walker Algorithm: Reliability and Agreement

Stevenson, G. N., Collins, S. L., Ding, J., Impey, L., & Noble, J. A. (2015) · Ultrasound in Medicine & Biology

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 46

Rapid Calculation of Standardized Placental Volume at 11 to 13 Weeks and the Prediction of Small for Gestational Age Babies

Collins, S. L., Stevenson, G. N., Noble, J. A., & Impey, L. (2013) · Ultrasound in Medicine & Biology

Pregnancy and preeclampsia studiesPrenatal Screening and Diagnostics

Cited by 39

A technique for the estimation of fractional moving blood volume by using three-dimensional power Doppler US

Stevenson, G. N., Collins, S. L., Welsh, A. W., Impey, L. W., & Noble, J. A. (2015) · Radiology

Medical Image Segmentation TechniquesUltrasound Imaging and Elastography

Cited by 38

Self-Supervised Contrastive Video-Speech Representation Learning for Ultrasound

Jiao, J., Cai, Y., Alsharid, M., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 38

Clinical workflow of sonographers performing fetal anomaly ultrasound scans: Deep-learning-based analysis

Drukker, L., Sharma, H., Karim, J. N., Droste, R., Noble, J. A., & Papageorghiou, A. T. (2022) · Ultrasound in Obstetrics & Gynecology

Prenatal Screening and DiagnosticsArtificial Intelligence in Healthcare and Education

Cited by 36

Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis

Maraci, M. A., Yaqub, M., Craik, R., Beriwal, S., Self, A., von Dadelszen, P., Papageorghiou, A., & Noble, J. A. (2020) · Journal of medical imaging

Artificial Intelligence in Healthcare and EducationUltrasound in Clinical Applications

Cited by 36

Expected‐value bias in routine third‐trimester growth scans

Drukker, L., Droste, R., Chatelain, P., Noble, J. A., & Papageorghiou, A. T. (2020) · Ultrasound in Obstetrics & Gynecology

Prenatal Screening and DiagnosticsUltrasound in Clinical Applications

Cited by 29

Gaze-assisted automatic captioning of fetal ultrasound videos using three-way multi-modal deep neural networks

Alsharid, M., Cai, Y., Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2022) · Medical Image Analysis

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 25