Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification

By Jason J. Corso, Eitan Sharon, S. Dube, S. El-Saden, U. Sinha, Alan L. Yuille. IEEE Transactions on Medical Imaging, 2008

489 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/16b2befc54363e8a1b56ad6b580c76b05b1f1fd4

How researchers see this paper

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

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Visual to Text: Survey of Image and Video Captioning (2019, background): “The features will be computed on objects and interest points as well as on the whole scene, salient scene segments, and super-pixels [8].”
Deep learning based brain tumor segmentation: a survey (2020, methodology): “[32], graphical models with intensity histograms [33], [34], tumor boundary detection from latent atlas [35].”
Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation (2014, methodology): “Comparison of (block A in table) state-of-the-art video segmentation algorithms [6, 14, 12, 29, 13], (block B) the proposed spectral graph reduction methods and (block C) the streaming variants, on the benchmark of [13].”
Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization (2011, result): “Dice similarity coefficients for the GTV are in a similar range as the results presented in [3] and [12], but different data was used.”
Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network (2018, methodology): “[5] 20 cases of in vivo brain tumors; T1, T1-C, T2, FLAIR A hybrid method combining an affinity-based segmentation method with a generative model 0.”
Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR (2012, methodology): “GT [7, 10, 16]; GT and E [4, 6, 12, 15]; AC, NC, E [2, 15]), input data, and the evaluation procedure.”
State of the art survey on MRI brain tumor segmentation. (2013, unknown): “(adapted from [3]).”
A Unified Video Segmentation Benchmark: Annotation, Metrics and Analysis (2013, background): “Video GT-1 GT-2 [7] [10] [13] [19] [29] Baseline Figure 6.”
Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data (2013, background): “This is largely because both the shape and properties of the brain are more homogeneous across individuals than is the case for other parts of the body; for example, segmentation of MS lesions is reported in [30], [31], [32], and [33] .”
Streaming Hierarchical Video Segmentation (2012, unknown): “To evaluate this progress and these methods’ comparative suitability for early video oversegmentation into supervoxels, Xu and Corso [10] study five methods, with varying characteristics (segmentation by weighted aggregation (SWA) [11, 12], graph-based (GB) [13], hierarchical graph-based (GBH) [3], ”
A survey of MRI-based brain tumor segmentation methods (2014, background): “2 Left is three types of brain tumor MRI images: T1 with contrast, T2 and FLAIR image; right is three main components after segmenting brain tumor[6].”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, unknown): “Section VI concludes the paper.”
Spatio-temporal Object Detection Proposals (2014, unknown): “SWA is a hierarchical segmentation method that solves normalized cuts at each level.”