Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

By M. Thaha, K. Pradeep, Mohan Kumar, B. Murugan, S. Dhanasekeran, P. Vijayakarthick, S. Selvi. Journal of medical systems, 2019

339 citations recorded by the source corpus.

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Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (2017, methodology): “Compared to conventional supervised machine learning methods , these deep learning based methods are not dependent on hand-crafted features, but automatically learn a hierarchy of increasingly complex features directly from data [21].”
Deep learning based brain tumor segmentation: a survey (2020, methodology): “Other research works such as [106,160] followed the pattern ofVGG[119] to build convolutional layerswith a small sized kernel (typically 3) as basic block.”
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions (2017, methodology): “Multiple studies have reported the data augmentation to be very useful in their studies [9, 22, 29].”
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, methodology): “Segmentation and shape modelling, such as brain tumour segmentation [63] or, more generally, brain structure segmentation [64].”
Radiomics with artificial intelligence: a practical guide for beginners. (2019, methodology): “To avoid this variability, a few automatic and semi-automatic methods have been described as follows: active contour (snake) methods (49), level set methods (50), region-based methods (51), graph-based methods (52), and deep learning-based methods (53).”