| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1775 | 2018 |
| Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI M Soltaninejad, G Yang, T Lambrou, N Allinson, TL Jones, TR Barrick, ... International journal of computer assisted radiology and surgery 12, 183-203, 2017 | 334 | 2017 |
| Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels M Soltaninejad, G Yang, T Lambrou, N Allinson, TL Jones, TR Barrick, ... Computer methods and programs in biomedicine 157, 69-84, 2018 | 202 | 2018 |
| MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks M Soltaninejad, L Zhang, T Lambrou, G Yang, N Allinson, X Ye Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 76 | 2018 |
| Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction M Amian, M Soltaninejad International MICCAI Brainlesion Workshop, 221-230, 2019 | 48 | 2019 |
| Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks M Soltaninejad, CJ Sturrock, M Griffiths, TP Pridmore, MP Pound IEEE Transactions on Image Processing, 2020 | 40 | 2020 |
| Brain tumour grading in different MRI protocols using SVM on statistical features M Soltaninejad, X Ye, G Yang, N Allinson, T Lambrou | 38 | 2014 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, 2022 | 32 | 2022 |
| Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network M Soltaninejad, L Zhang, T Lambrou, N Allinson, X Ye arXiv preprint arXiv:1704.08134, 2017 | 30 | 2017 |
| Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks. M Soltaninejad, TP Pridmore, MP Pound BrainLes@ MICCAI (2020). Lecture Notes in Computer Science, Springer, Cham …, 2020 | 18 | 2020 |
| X‐ray CT reveals 4D root system development and lateral root responses to nitrate in soil M Griffiths, N Mellor, CJ Sturrock, BS Atkinson, J Johnson, S Mairhofer, ... The Plant Phenome Journal 5 (1), e20036, 2022 | 14 | 2022 |
| MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks M Soltaninejad, L Zhang, T Lambrou, G Yang, N Allinson, X Ye 2017 International MICCAI BraTS Challenge, 279-283, 2017 | 13 | 2017 |
| An image analysis approach to MRI brain tumour grading M Soltaninejad, X Ye, G Yang, N Allinson, T Lambrou Oncol. News 9 (6), 204-207, 2015 | 11 | 2015 |
| A Hybrid Method for Haemorrhage Segmentation in Trauma Brain CT. M Soltaninejad, T Lambrou, A Qureshi, NM Allinson, X Ye MIUA, 99-104, 2014 | 9 | 2014 |
| Automatic crown surface reconstruction using tooth statistical model for dental prosthesis planning MR Soltaninejad, RA Zoroofi, G Shirani 2012 19th Iranian Conference of Biomedical Engineering (ICBME), 218-222, 2012 | 3 | 2012 |
| Xujiong Ye. MRI brain tumor segmentation and patient survival prediction using random forests and fully convolutional networks M Soltaninejad, L Zhang, T Lambrou, G Yang, N Allinson International MICCAI Brainlesion Workshop, 204-215, 0 | 2 | |
| Supervised learning-based multimodal MRI brain image analysis M Soltaninejad University of Lincoln, 2017 | 1 | 2017 |
| Brain Tumor Detection using Tree-based Representation of Fuzzy Sets in MR Images HA Sheibani, MR Soltaninejad, AH Jafari 11th Iranian Conference on Intelligent Systems, Tehran, Iran, 2013 | | 2013 |
| Brain Tumour Grading in Different MRI Protocols using SVM on Statistical M Soltaninejad, X Ye, G Yang, N Allinson, T Lambrou | | 2013 |
| Quantification of Teeth in CT Images Using Statistical Shape Model Based on Geometrical Complexity MR Soltaninejad, RA Zoroofi, G Shirani 26th International Conference Image and Vision Computing New Zealand, At …, 2011 | | 2011 |
Tryphon LambrouSenior Lecturer in Computing Science, University of AberdeenVerified email at ieee.org
