A brain tumor segmentation framework based on outlier detection

By Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig. Medical Image Anal., 2004

709 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/64bda8b75fcb70e60efc6816888fa7a0368f2c96

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In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

A survey of MRI-based brain tumor segmentation methods (2014, background): “For example, a knowledge-based segmentation algorithm by combining pixel-intensity distributions and level set snakes is presented [122] .”
A Structural MRI Study of Human Brain Development from Birth to 2 Years (2008, methodology): “We used a well established automatic brain tissue segmentation method (expectation maximization segmentation) which integrates bias correction, brain stripping, and tissue classification into one tool (Van Leemput et al., 1999; Prastawa et al., 2004).”
Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR (2012, methodology): “Sensitivity to Variation of Parameters is tested by varying n ∈ [15 , 40] and d ∈ [12 , 20], for the ten 30/10-tests.”
Population Shape Regression from Random Design Data (2007, methodology): “The tissue exterior to the brain was removed using a mask generated by a brain segmentation tool described in [31].”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “Local regularity of tissue labels can be encoded via boundary modeling for both generative [17], [55] and discriminative models [32], [33], [35], [55], [56], potentially enforcing non-local shape constraints [57].”
Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification (2008, neutral): “In general, it is impossible to segment a GBM tumor by simple thresholding techniques [4].”
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images (2016, methodology): “These metrics are commonly used in medical image segmentation tasks [9,25].”
Outlier Analysis (2013, methodology): “General references on image outlier detection may be found in [68, 206, 374, 466, 393, 418, 446, 447].”
Reconstructing subject‐specific effect maps (2017, methodology): “stributions, this procedure essentially computes normalized z-scores with outliers having the highest values. Outlier detection has been used for detecting brain lesions and neurodegenerative changes [36,38,30,41]. The main drawback of this approach is its unspecic nature. Outlier detection identies”
Unsupervised Brain Abnormality Detection Using High Fidelity Image Reconstruction Networks (2020, methodology): “These approaches have employed stochastic intensity models (Van Leemput et al., 2001), atlas-based registration (Prastawa et al., 2004), clustering (Shiee et al., 2010), content-based retrieval (Weiss et al., 2013), and statistical models based on image registration (Tomas-Fernandez and Warﬁeld,…”
DeepMedic for Brain Tumor Segmentation (2016, background): “Representative early work is the atlas-based outlier detection method [3].”