Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features

Pinto, Adriano; Pereira, Sérgio; Correia, Higino; Oliveira, Jorge; Rasteiro, Deolinda M. L. D.; Silva, Carlos A.

doi:10.1109/embc.2015.7319032

Cited by 86 publications

(46 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies on brain tumor segmentation can be roughly categorized into un-supervised learning based [9][10][11][12] and supervised learning based [13][14][15][16][17][18] methods. A more detailed topical review on various brain tumor segmentation methods can be found elsewhere, e.g., in [6].…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al [13] employed superpixel features in a conditional random fields framework to segment brain tumors, but the results varied significantly among different patient cases and especially underperformed in LGG images. A study was proposed in which extremely randomized forest was used for classifying both appearance and context based features and 83% Dice score was achieved [14]. More recently, Soltaninejad et al [16] combined extremely randomized trees classification with superpixel based over-segmentation for a single FLAIR sequence based MRI scan that obtained 88% overall Dice score of the complete tumor segmentation for both LGG and HGG tumor cases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Dong

Yang

Liu

et al. 2017

Communications in Computer and Information Science

639

367

View full text Add to dashboard Cite

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Crossvalidation has shown that our method can obtain promising segmentation efficiently.MIUA2017, 069, v3: 'Automatic Brain Tumor Detection and Segmentation Using U-Net . . .

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Dong

Yang

Liu

et al. 2017

Communications in Computer and Information Science

639

367

View full text Add to dashboard Cite

show abstract

“…Recently, random forests (RF) and convolutional neural networks (CNN) have drawn more interest. Some researchers have successfully applied these two groups of algorithms to brain tumor segmentation for MRI images . Random forests represent machine learning algorithms that allow for the tumor classification.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have successfully applied these two groups of algorithms to brain tumor segmentation for MRI images. 4,22,[25][26][27][28][29][30] Random forests represent machine learning algorithms that allow for the tumor classification. Many decision trees are constructed during training, and a class is assigned that claims the most votes of the individual trees during testing.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using holistically nested neural networks in MRI images

et al. 2017

View full text Add to dashboard Cite

Purpose Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic Resonance Imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 years of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically-nested neural networks that could be employed for brain tumor segmentation of MRI images. Methods Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically-nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multi-scale and multi-level hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. Results The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training data sets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training data sets with High Grade Gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to 10 clinical data sets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 hours for training with 50,000 iterations, and approximately 30 seconds for testing of a typical MRI image in the BRATS 2013 data set with a size of 160×216×176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. Conclusions An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy.

show abstract

“…Helen et al developed a hybrid method for brain tumour segmentation based on clustering, classification and conventional segmentation methods [11]. Several works applied random forests (RF) classification and its variants to segment tumours [12][13][14][15]. In [12] several features including intensity, geometry and asymmetry from multiple modalities are applied to a random forests classifier.…”

Section: Related Workmentioning

confidence: 99%

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

Soltaninejad

Yang

Lambrou

et al. 2018

Computer Methods and Programs in Biomedicine

189

View full text Add to dashboard Cite

show abstract

Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features

Cited by 86 publications

References 8 publications

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Brain tumor segmentation using holistically nested neural networks in MRI images

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

Contact Info

Product

Resources

About