Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization

By Stefan Bauer, Lutz-Peter Nolte, Mauricio Reyes. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011

388 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/4e607b44337fe7160cb4f4ce153332c7e53964a7

How researchers see this paper

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

In Biophysics

1. David Warde-Farley
2. A. Işın
3. Melike Šah
4. Cem Direkoǧlu
5. Ben Glocker
6. Yoshua Bengio
7. Aaron C. Courville
8. Hugo Larochelle

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ming-Hsuan Yang
4. Trevor Darrell
5. Xiaogang Wang
6. Jitendra Malik
7. Andrew Zisserman
8. Bernt Schiele

In Neurology

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

Citations in the corpus

• A survey of MRI-based brain tumor segmentation methods (2014, methodology): “A fully automatic method for brain tissue segmentation was proposed [105] , which combined SVM classification using multispectral intensities and textured with subsequent hierarchical regularization based on CRF.”
• Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images (2016, methodology): “To overcome this problem, some authors include information of the neighborhood by embedding the probabilistic predictions of the classifier into a Conditional Random Field [12]–[15].”
• Brain tumor segmentation with Deep Neural Networks (2015, methodology): “Classical discriminative learning techniques such as SVMs [4, 41, 30] and decision forests [48] have also been used.”
• Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR (2012, methodology): “To provide some indicative context, we cite results of a recent work from [2].”
• Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network (2018, methodology): “These methods can be largely divided into two categories: (1) hand-crafted feature and classi fi er methods based on traditional machine learning such as support vector machine (SVM) and random forests (RF) [5 – 13] and (2) fully automatic methods based on deep learning using the CNN [14 – 21].”
• Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods (2016, methodology): “Conditional random fields (CRF) 23, 24 and connected components (CC) 27, 29 are among the popular choices.”