Hierarchical brain tumour segmentation using extremely randomized trees

Pinto, Adriano; Pereira, Sérgio; Rasteiro, Deolinda M. L. D.; Silva, Carlos A.

doi:10.1016/j.patcog.2018.05.006

Cited by 79 publications

(41 citation statements)

References 30 publications

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“…In Table III we present the results on BRATS 2013 Leaderboard and Challenge sets. Even though these datasets are older and smaller, they allow us to compare with a larger variety of methods, such as CNN-based [6], [37], [38], [39], Random Forest and handcrafted features-based methods [40], [26], and multi-atlas patch-based methods [41]. Comparing our Baseline with the FCN with RR SegSE block, we can observe that the latter achieves an overall better performance in terms of DC in both the Leaderboard and Challenge set.…”

Section: Baseline + Rr Segse Baseline + Rr Var 2 Baseline + Rr Var mentioning

confidence: 99%

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks

Pereira

Pinto

Amorim

et al. 2019

IEEE Trans. Med. Imaging

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Fully Convolutional Networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same relevance for the classes being predicted. In order to learn the inter-channel relationships and recalibrate the channels to suppress the less relevant ones, Squeeze and Excitation blocks were proposed in the context of image classification with Convolutional Neural Networks. However, this is not well adapted for segmentation with Fully Convolutional Networks since they segment several objects simultaneously, hence a feature map may contain relevant information only in some locations. In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks -the SegSE block. Feature maps are recalibrated by considering the cross-channel information together with spatial relevance. Experimental results indicate that Recombination and Recalibration improve the results of a competitive baseline, and generalize across three different problems: brain tumor segmentation, stroke penumbra estimation, and ischemic stroke lesion outcome prediction. The obtained results are competitive or outperform the state of the art in the three applications.

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Section: Baseline + Rr Segse Baseline + Rr Var 2 Baseline + Rr Var mentioning

confidence: 99%

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks

Pereira

Pinto

Amorim

et al. 2019

IEEE Trans. Med. Imaging

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“…In addition, they design an ensemble learning algorithm for best segmentation accuracy. Pinto, Pereira, Rasteiro, and Silva () described a hierarchical framework for brain tumor segmentation through extreme randomize trees and context features. They used BRATS 2013 challenge data set and achieve notable performance.…”

Section: Related Workmentioning

confidence: 99%

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

149

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Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

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“…It consists of an ensemble of various single decision trees for subset data, which are resampled randomly from the training data [76]. The ET is an ensemble of a certain number of randomized trees that adds more randomization to the RF [73,77]. Both RF and ET are computationally efficient and capable of handling very high-dimensional features [75,77].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…The ET is an ensemble of a certain number of randomized trees that adds more randomization to the RF [73,77]. Both RF and ET are computationally efficient and capable of handling very high-dimensional features [75,77]. However, the ET has a shorter training time than the RF in that it takes a simple approach to select thresholds on the node.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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Reconstruction of Ocean Color Data Using Machine Learning Techniques in Polar Regions: Focusing on Off Cape Hallett, Ross Sea

Park

Kim

et al. 2019

Remote Sensing

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The most problematic issue in the ocean color application is the presence of heavy clouds, especially in polar regions. For that reason, the demand for the ocean color application in polar regions is increased. As a way to overcome such issues, we conducted the reconstruction of the chlorophyll-a concentration (CHL) data using the machine learning-based models to raise the usability of CHL data. This analysis was first conducted on a regional scale and focused on the biologically-valued Cape Hallett, Ross Sea, Antarctica. Environmental factors and geographical information associated with phytoplankton dynamics were considered as predictors for the CHL reconstruction, which were obtained from cloud-free microwave and reanalysis data. As the machine learning models used in the present study, the ensemble-based models such as Random forest (RF) and Extremely randomized tree (ET) were selected with 10-fold cross-validation. As a result, both CHL reconstructions from the two models showed significant agreement with the standard satellite-derived CHL data. In addition, the reconstructed CHLs were close to the actual CHL value even where it was not observed by the satellites. However, there is a slight difference between the CHL reconstruction results from the RF and the ET, which is likely caused by the difference in the contribution of each predictor. In addition, we examined the variable importance for the CHL reconstruction quantitatively. As such, the sea surface and atmospheric temperature, and the photosynthetically available radiation have high contributions to the model developments. Mostly, geographic information appears to have a lower contribution relative to environmental predictors. Lastly, we estimated the partial dependences for the predictors for further study on the variable contribution and investigated the contributions to the CHL reconstruction with changes in the predictors.

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Hierarchical brain tumour segmentation using extremely randomized trees

Cited by 79 publications

References 30 publications

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Reconstruction of Ocean Color Data Using Machine Learning Techniques in Polar Regions: Focusing on Off Cape Hallett, Ross Sea

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