State of the art survey on MRI brain tumor segmentation.

By N. Gordillo, Eduard Montseny, Pilar Sobrevilla. Magnetic Resonance Imaging, 2013

709 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/80b2dba230f924a319889cd9cf9411687efc45f7

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In Neurology

Citations in the corpus

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM (2017, methodology): “Brain tumor segmentation involves the process of separating the tumor tissues such as edema and dead cells from normal brain tissues and solid tumors, such as WM, GM, and CSF [4] with the help of MR images or other imaging modalities [5–8].”
Deep learning based brain tumor segmentation: a survey (2020, methodology): “State of the art survey on MRI brain tumor segmentation [17] MRI 2013 Summary of segmentation methods before 2013 A survey of MRI-based brain tumor segmentation methods [18] Tsinghua Science and Technology 2014 Survey of methods used for brain MRI segmentation before 2014 A survey on deep learning i”
Computer Vision, Graphics, and Image Processing (2016, methodology): “In [10], salient object is detected using eigenvalue analysis of a graph Laplacian which is defined over color similarity in image superpixels with the assumption that the most of the image boundary pixel are covered by non-salient background.”
Brain tumor detection and classification using machine learning: a comprehensive survey (2021, methodology): “As manual segmentation process is erroneous [110]; therefore, semi-and fully automated methods are utilized [46].”
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition (2015, background): “Recent studies have proposed numerous automatic and semi-automatic techniques for the detection and segmentation of brain tumors [1 – 3].”
Learning Active Learning from Data (2017, methodology): “The performance metrics are task-specific and include classification accuracy, IOU [6], dice score [8], AMS score [1], as well as area under the ROC curve (AUC).”
Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods (2016, methodology): “Detailed reviews of these methods were previously presented 6, 12, 13, 14 .”
State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, background): “The value determines a delineated area to identify it as a pathological tissue [57]–[59].”