Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

By Sérgio Pereira, Adriano Pinto, Victor Alves, Carlos Alberto Silva. IEEE Transactions on Medical Imaging, 2016

2,370 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/420f3f1078a6d8e0696572c032877079286051c6

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Citations in the corpus

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation (2017, methodology): “In this subsection, we compare the proposed method, our S3-3C SegAN model, with other state-of-the-art methods on the BRATS 2013 Leaderboard [11, 26] Test and the BRATS 2015 Test [13].”
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, methodology): “Specifically for medical image segmentation, CNNs have been applied to segment brain tumor on MR images [23], urinary bladder on CT urolography [24] left ventricle on cardiac MRI [25], and skin lesion from nondermoscopic images [26] among others.”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, methodology): “It is also common to use hand-tuned data augmentation to increase the number of training examples [2, 55, 57, 63, 65].”
Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI (2017, neutral): “, 2016a,b) and various brain abnormality segmentation tasks (Pereira et al., 2016; Brosch et al., 2016; Havaei et al., 2017, 2016; Kamnitsas et al., 2017; Ghafoorian et al., 2017a,b; Valverde et al., 2017).”
State-of-the-art review on deep learning in medical imaging. (2019, neutral): “[48] MRI (339) Brain tumor Segmentation CNN1 (HGG) + CNN2 (LGG) DM complete: 0.”
Quo vadis artificial intelligence? (2022, background): “For instance, AI helps to markup suspicious lesion areas on optical images [106], X-ray images [107], or magnetic resonance images [108–110], and can analyze patients’ speech to diagnose Alzheimer’s disease [111].”
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, background): “Various manifestations and classes of brain tumors have different appearances on magnetic resonance imaging (MRI) data [4,5].”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “2D - CONVOLUTIONAL NEURAL NETWORKS Nowadays, deep 2D networks are applied to resolve a wide range of computer vision obstacles such as image segmentation [28], medical image classification [29], and face recognition [30].”