Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications
In this paper, we present a fast and robust practical tool for segmentation of solid tumors with
minimal user interaction to assist clinicians and researchers in radiosurgery planning and …
minimal user interaction to assist clinicians and researchers in radiosurgery planning and …
Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012–2018 challenges
M Ghaffari, A Sowmya, R Oliver - IEEE reviews in biomedical …, 2019 - ieeexplore.ieee.org
Reliable brain tumor segmentation is essential for accurate diagnosis and treatment
planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive …
planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive …
Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images
A local image model is proposed to eliminate the adverse impact of both artificial and
inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based …
inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based …
Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy
In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to
fully automatic segmentation of Glioblastoma brain tumors with high-and low-grade. The …
fully automatic segmentation of Glioblastoma brain tumors with high-and low-grade. The …
A brain tumor segmentation framework based on outlier detection
M Prastawa, E Bullitt, S Ho, G Gerig - Medical image analysis, 2004 - Elsevier
This paper describes a framework for automatic brain tumor segmentation from MR images.
The detection of edema is done simultaneously with tumor segmentation, as the knowledge …
The detection of edema is done simultaneously with tumor segmentation, as the knowledge …
[PDF][PDF] Context-sensitive classification forests for segmentation of brain tumor tissues
We describe our submission to the Brain Tumor Segmentation Challenge (BraTS) at MICCAI
2012, which is based on our method for tissue-specific segmentation of high-grade brain …
2012, which is based on our method for tissue-specific segmentation of high-grade brain …
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images
A new algorithm is presented for the automatic segmentation of Multiple Sclerosis (MS)
lesions in 3D Magnetic Resonance (MR) images. It builds on a discriminative random …
lesions in 3D Magnetic Resonance (MR) images. It builds on a discriminative random …
MRI brain tumor segmentation and patient survival prediction using random forests and fully convolutional networks
In this paper, we propose a learning based method for automated segmentation of brain
tumor in multimodal MRI images, which incorporates two sets of machine-learned and hand …
tumor in multimodal MRI images, which incorporates two sets of machine-learned and hand …
[PDF][PDF] Extremely randomized trees based brain tumor segmentation
M Goetz, C Weber, J Bloecher, B Stieltjes… - Proceeding of BRATS …, 2014 - researchgate.net
Random Decision Forest-based approaches have previously shown promising performance
in the domain of brain tumor segmentation. We extend this idea by using an ExtraTree …
in the domain of brain tumor segmentation. We extend this idea by using an ExtraTree …