Random decision forests for automatic brain tumor segmentation on multi-modal MRI images

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

doi:10.1109/enbeng.2015.7088842

Cited by 20 publications

(9 citation statements)

References 11 publications

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“…The random generated vector Θ k determines both the voxel subset and the nodal feature subsets associated with the tree. Its randomness promotes diversity among the tree classifiers [14]. …”

Section: Methodsmentioning

confidence: 99%

Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling

Deng

Yap

et al. 2016

Machine Learning in Medical Imaging

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Brain magnetic resonance image segmentation is one of the most important tasks in medical image analysis and has considerable importance to the effective use of medical imagery in clinical and surgical setting. In particular, the tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain measurement and disease diagnosis. A variety of studies have shown that the learning-based techniques are efficient and effective in brain tissue segmentation. However, the learning-based segmentation methods depend largely on the availability of good training labels. The commonly used 3T magnetic resonance (MR) images have insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF, therefore not able to provide good training labels for learning-based methods. The advances in ultra-high field 7T imaging make it possible to acquire images with an increasingly high level of quality. In this study, we propose an algorithm based on random forest for segmenting 3T MR images by introducing the segmentation information from their corresponding 7T MR images (through semi-automatic labeling). Furthermore, our algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers to improve the tissue segmentation. Experimental results on 10 subjects with both 3T and 7T MR images in a leave-one-out validation, show that the proposed algorithm performs much better than the state-of-the-art segmentation methods.

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Section: Methodsmentioning

confidence: 99%

Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling

Deng

Yap

et al. 2016

Machine Learning in Medical Imaging

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“…In brain tumor segmentation, where tumor regions are often scattered all over the image, pixel classification rather than classical segmentation methods are often preferable [65]. Therefore, the traditional supervised machine learning algorithms have been used in the segmentation of a brain tumor from a head MRI scan [66][67][68][69][70][71][72][73][74][75][76]. In this section, as shown in Table 4, most relevant literature on brain tumor segmentation using traditional machine learning algorithms, such as support vector machine (SVM), artificial neural network (ANN), random forest (RF) are surveyed to identify data used, the pre-processing, feature extraction techniques, the classifier model, and whether or not post-processing is implemented.…”

Section: Supervised Shallow Machine Learning Based Approachmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

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A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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“…Its randomness promotes diversity among the tree classifiers. 40 Important parameters of the random forest classifier are the number of trees, the maximum tree depth, the number of random thresholds for each feature, the total number of random input features, and the minimum sample size for each leaf node. These parameters are typically set heuristically or by trial and error.…”

Section: C Random Forestmentioning

confidence: 99%