Rueckert, Daniel

Segmentation of subcortical structures and the hippocampus in brain MRI using graph-cuts and subject-specific a-priori information

Wolz, R., Aljabar, P., Rueckert, D., Heckemann, R. A., & Hammers, A. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 28

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging

Turgut, Ö., Müller, P., Hager, P., Shit, S., Starck, S., Menten, M. J., Martens, E., & Rueckert, D. (2025) · Medical Image Analysis

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 22

Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework

Rao, A., Ledig, C., Newcombe, V., Menon, D., & Rueckert, D. (2014) · 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 18

Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI Pooling

Müller, P., Meissen, F., Kaissis, G., & Rueckert, D. (2025) · IEEE Transactions on Medical Imaging

COVID-19 diagnosis using AIAdvanced Neural Network Applications

Cited by 8

A deep-learning framework reveals whole-body perturbations at cell level

Kaltenecker, D., Horvath, I., Al-Maskari, R., Chen, Y., Kolabas, Z. I., Hoeher, L., Todorov, M., Minde, D.-P., Kapoor, S., Turhan, S. G., et al. (2026) · Nature

Cell Image Analysis TechniquesSingle-cell and spatial transcriptomics

Cited by 5