A survey of MRI-based medical image analysis for brain tumor studies

By Stefan Bauer, Roland Wiest, Lutz-Peter Nolte, Mauricio Reyes. Physics in Medicine and Biology, 2013

995 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c83c72d26ca8d4152150f1ccbbb180542662cdfa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ming-Hsuan Yang
4. Xiaogang Wang
5. Daniel Rueckert
6. Trevor Darrell
7. Andrew Zisserman
8. Alan L. Yuille

In Neurology

1. A. Işın
2. Melike Šah
3. Cem Direkoǧlu
4. Sérgio Pereira
5. Carlos Alberto Silva
6. Victor Alves
7. Adriano Pinto
8. Ben Glocker

In Radiology, Nuclear Medicine and Imaging

1. Bernhard Kainz
2. D. Louis Collins
3. Ben Glocker
4. Nicholas J. Tustison
5. Daniel Rueckert
6. K. V. Leemput
7. Doina Precup
8. Nicholas Ayache

Citations in the corpus

• Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods (2016, methodology): “Features like, asymmetry-related features 19 , discrete wavelet transforms 20 (DWT), textons 21 , multifractal Brownian motion features 22 , first order statistical features 19 , raw intensities, local image textures, intensity gradients and edge based features 12 are some examples.”
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation (2017, background): “High grade gliomas (HGG) develop rapidly and aggressively, forming abnormal vessels and often a necrotic core, accompanied by surrounding oedema and swelling [2].”
• Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network (2018, background): “According to US National Cancer Institute, approximately 18,000 Americans are diagnosed with a glioma every year; many of them die within 14 months [2].”
• A survey of MRI-based brain tumor segmentation methods (2014, background): “In the clinic, brain tumor segmentation methods are usually classified into three main categories including manual, semi-automatic, and fully automatic segmentations based on the degree of required human interaction [31-33] .”
• Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine (2018, neutral): “There are semiautomatic and fully automatic approaches of various complexity, from simple thresholding or region growing algorithms to comprehensive modelbased, supervised, and unsupervised machine learning algorithms.(10,11) To compare the different approaches to brain tumor segmentation algorithms”
• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “SHOWN IS A SELECTION OF ALGORITHMS DISCUSSED HERE AND IN [7].”
• Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images (2016, methodology): “These neoplasms can be graded into Low Grade Gliomas (LGG) and High Grade Gliomas (HGG), with the former being less aggressive and infiltrative than the latter [1], [2].”