Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel M.; Jones, Timothy L.; Barrick, Thomas R.; Howe, Franklyn A.; Ye, Xujiong

doi:10.1007/s11548-016-1483-3

Cited by 256 publications

(143 citation statements)

References 42 publications

Supporting

Mentioning

133

Contrasting

Unclassified

Order By: Relevance

“…In addition, it implies that the image acquisition time can be reduced since one imaging modality is enough to do the diagnostic classification. In [38], the authors also used only FLAIR MR images for detecting and segmenting brain tumors. The results show that with the use of only one imaging modality, FLAIR MR, they were able to obtain accurate performance with a classification precision of 87.86% and a sensitivity of 89.48%.…”

Section: Resultsmentioning

confidence: 99%

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

et al. 2017

View full text Add to dashboard Cite

Abstract:In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

show abstract

Section: Resultsmentioning

confidence: 99%

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Superpixel processing was coupled with support vector machines and extremely randomized trees in [40]. Although the results appeared promising, the authors did not report the computation time nor cost of their method, and did not provide any insights into the classifier parameters.…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

Nalepa

Lorenzo

Marcinkiewicz³

et al. 2020

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human error. In this paper, we propose a fullyautomated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark (BraTS'17 for tumor segmentation, and a test dataset released by the Quantitative Imaging * Corresponding author Biomarkers Alliance for the contrast-concentration fitting) and clinical (44 lowgrade glioma patients) data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a realtime algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results (in terms of segmentation accuracy and contrastconcentration fitting) while requiring less than 3 minutes to process an entire input DCE-MRI study using a single GPU.

show abstract

“…We applied a SLIC (Simple Linear Iterative Clustering) based SV method, which has been proved to be effective and efficient in many medical image analysis problems [9][10][11] . SV is an unsupervised learning 12 based method that over-segments the images into meaningful sub-regions.…”

Section: Semi-automated Super-voxel Refinementmentioning

confidence: 99%

Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

et al. 2017

Self Cite

View full text Add to dashboard Cite

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is a non-invasive technique, which has shown promise in detecting native and post-ablation atrial scarring. To visualize the scarring, a precise segmentation of the left atrium (LA) and pulmonary veins (PVs) anatomy is performed as a first step-usually from an ECG gated CMRI roadmap acquisition-and the enhanced scar regions from the LGE CMRI images are superimposed. The anatomy of the LA and PVs in particular is highly variable and manual segmentation is labor intensive and highly subjective. In this paper, we developed a multi-atlas propagation based whole heart segmentation (WHS) to delineate the LA and PVs from ECG gated CMRI roadmap scans. While this captures the anatomy of the atrium well, the PVs anatomy is less easily visualized. The process is therefore augmented by semi-automated manual strokes for PVs identification in the registeredLGE CMRI data. This allows us to extract more accurate anatomy than the fully automated WHS. Both qualitative visualization and quantitative assessment with respect to manual segmented ground truth showed that our method is efficient and effective with an overall mean Dice score of 0.91.

show abstract

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

Cited by 256 publications

References 42 publications

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

Contact Info

Product

Resources

About