Rueckert, Daniel

Prediction of stroke thrombolysis outcome using CT brain machine learning

Bentley, P., Ganesalingam, J., Carlton Jones, A. L., Mahady, K., Epton, S., Rinne, P., Sharma, P., Halse, O., Mehta, A., & Rueckert, D. (2014) · NeuroImage: Clinical

Acute Ischemic Stroke ManagementArtificial Intelligence in Healthcare

Cited by 184

Medical imaging deep learning with differential privacy

Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., & Kaissis, G. (2021) · Scientific Reports

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 172

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai, W., Shi, W., de Marvao, A., Dawes, T. J. W., O’Regan, D. P., Cook, S. A., & Rueckert, D. (2015) · Medical Image Analysis

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 171

Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study

Monteiro, M., Newcombe, V. F. J., Mathieu, F., Adatia, K., Kamnitsas, K., Ferrante, E., Das, T., Whitehouse, D., Rueckert, D., Menon, D. K., et al. (2020) · The Lancet Digital Health

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 162

Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2017) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 152

Multi-modal learning from unpaired images: application to multi-organ segmentation in CT and MRI

Valindria, V. V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 146

Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration

Russakoff, D. B., Rohlfing, T., Mori, K., Rueckert, D., Ho, A., Adler, J. R., & Maurer, C. R. (2005) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 142

Injury markers predict time to dementia in subjects with MCI and amyloid pathology

van Rossum, I. A., Vos, S. J. B., Burns, L., Knol, D. L., Scheltens, P., Soininen, H., Wahlund, L.-O., Hampel, H., Tsolaki, M., Minthon, L., et al. (2012) · Neurology

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 136

Automatic quantification of normal cortical folding patterns from fetal brain MRI

Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M. A., Hajnal, J. V., Rueckert, D., & Aljabar, P. (2014) · NeuroImage

Medical Image Segmentation TechniquesFetal and Pediatric Neurological Disorders

Cited by 134

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Bernard, O., Bosch, J. G., Heyde, B., Alessandrini, M., Barbosa, D., Camarasu-Pop, S., Cervenansky, F., Valette, S., Mirea, O., Bernier, M., et al. (2016) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiovascular Function and Risk Factors

Cited by 110

Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment

Clerx, L., van Rossum, I. A., Burns, L., Knol, D. L., Scheltens, P., Verhey, F., Aalten, P., Lapuerta, P., van de Pol, L., van Schijndel, R., et al. (2013) · Neurobiology of Aging

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 98

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

Suinesiaputra, A., Dhooge, J., Duchateau, N., Ehrhardt, J., Frangi, A. F., Gooya, A., Grau, V., Lekadir, K., Lu, A., Mukhopadhyay, A., et al. (2018) · IEEE Journal of Biomedical and Health Informatics

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 95

Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

Åkerlund, C. A. I., Holst, A., Stocchetti, N., Steyerberg, E. W., Menon, D. K., Ercole, A., Nelson, D. W., the CENTER-TBI Participants and Investigators, Åkerlund, C., Amrein, K., et al. (2022) · Critical Care

Traumatic Brain Injury and Neurovascular DisturbancesTraumatic Brain Injury Research

Cited by 93

Automated fetal brain segmentation from 2D MRI slices for motion correction

Keraudren, K., Kuklisova-Murgasova, M., Kyriakopoulou, V., Malamateniou, C., Rutherford, M. A., Kainz, B., Hajnal, J. V., & Rueckert, D. (2014) · NeuroImage

Medical Image Segmentation TechniquesFetal and Pediatric Neurological Disorders

Cited by 91

Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C., Rutherford, M., Hajnal, J. V., Glocker, B., Rueckert, D., et al. (2016) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 77

Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain

Fenchel, D., Dimitrova, R., Seidlitz, J., Robinson, E. C., Batalle, D., Hutter, J., Christiaens, D., Pietsch, M., Brandon, J., Hughes, E. J., et al. (2020) · Cerebral Cortex

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and Applications

Cited by 71

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

Hou, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., Glocker, B., & Kainz, B. (2017) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesAdvanced MRI Techniques and Applications

Cited by 68

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Tong, T., Ledig, C., Guerrero, R., Schuh, A., Koikkalainen, J., Tolonen, A., Rhodius, H., Barkhof, F., Tijms, B., Lemstra, A. W., et al. (2017) · NeuroImage: Clinical

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 54

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

Meng, Q., Housden, J., Matthew, J., Rueckert, D., Schnabel, J. A., Kainz, B., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., et al. (2019) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 52

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time

Matthew, J., Skelton, E., Day, T. G., Zimmer, V. A., Gomez, A., Wheeler, G., Toussaint, N., Liu, T., Budd, S., Lloyd, K., et al. (2022) · Prenatal Diagnosis

Artificial Intelligence in Healthcare and EducationPrenatal Screening and Diagnostics

Cited by 41

Virtual reality-empowered deep-learning analysis of brain cells

Kaltenecker, D., Al-Maskari, R., Negwer, M., Hoeher, L., Kofler, F., Zhao, S., Todorov, M., Rong, Z., Paetzold, J. C., Wiestler, B., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesSingle-cell and spatial transcriptomics

Cited by 38

Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging

Robinson, R., Valindria, V. V., Bai, W., Suzuki, H., Matthews, P. M., Page, C., Rueckert, D., & Glocker, B. (2017) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 37

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

Ktena, S. I., Schirmer, M. D., Etherton, M. R., Giese, A.-K., Tuozzo, C., Mills, B. B., Rueckert, D., Wu, O., & Rost, N. S. (2019) · Stroke

Functional Brain Connectivity StudiesAcute Ischemic Stroke Management

Cited by 32

Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Ktena, S. I., Schirmer, M. D., Etherton, M. R., Giese, A.-K., Tuozzo, C., Mills, B. B., Rueckert, D., Wu, O., & Rost, N. S. (2019) · Stroke

Functional Brain Connectivity StudiesAcute Ischemic Stroke Management

Cited by 32