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Simonyan, K. and Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015, San Diego, 7-9 May 2015.
https://arxiv.org/abs/1409.1556v6

has been cited by the following article:

• TITLE: From Traditional Methods to 3D U-Net: A Comprehensive Review of Brain Tumour Segmentation Techniques

  AUTHORS: Mushtaq Mahyoob Saleh, Musab Elkheir Salih, Mohamed A. A. Ahmed, Altahir Mohamed Hussein

  KEYWORDS: Brain Tumour, MRI Modalities, Deep Learning, 3D U-Net, BraTS

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.18 No.1, January 31, 2025

  ABSTRACT: Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.