Atlas to patient registration with brain tumor based on a mesh-free method

Diaz, Idanis; Boulanger, Pierre

doi:10.1109/embc.2015.7319004

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…In case of segmentation in multiple regions, (Al-Shaikhli, Yang, & Rosenhahn, 2014) used an adapted multileveled set of articulation with the atlas data of the statistical provost graph. Diaz and Boulanger (2015) used Total Lagrangian Explicit Dynamic (TLED) method to tackle a massive disfiguration devoid of giving up. The atlases for the deformed brain image employ the genuine pattern of the tumor rather than employing erratic pattern (Dia & Boulanger, 2015).…”

Section: Atlas Methodsmentioning

confidence: 99%

Self – activated segmentation practices of brain tumefaction in MR scan images: a study

Perumal¹,

Devi²,

Rajasekaran³

2021

3C Tecnología

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To segment a tumefaction of brain images obtained from any of the imaging modalities is a lofty goal owing to the varied shape, locality and measure of tumor. This segmentation process can be done manually by a Doctor or otherwise can be done automatically using computer aided diagnosis. Self -Activated segmentation of brain tumor is nothing but the separation of the tissues that are not related with the tissues within the brain case akin to the regions with myelinated nerve fibers without dendrites, portions of nerve fibers with dendrites and the cerebrum area along with the cerebrospinal fluid (CSF). The various imaging modalities are the scans from the radioscopy with emitting positrons (PET), multiple X -rays (CT) and through Magnetic Resonance (MRI). In this paper, an overview of recent automatic brain tumor segmentation techniques of MRI and the advantages of multimodal imaging techniques has been explained. The segmentation techniques such as thresholding, edge based, morphology based, watershed, k means and markov random method are the conventional tactics of segmentation that are addressed. Also, the advanced segmentation methods such as region growing, genetic method, fuzzy clustering, deformation, atlas method and artificial neural network are also discussed. Moreover, the hybrid methods that have different combination of genetic algorithm, artificial neural networks and SVM are also considered. Among all the methods, the hybrid methods are found to be better as they provide the beneficiary factors of every method involved. But one should be aware about the algorithm's robustness and accuracy.

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Section: Atlas Methodsmentioning

confidence: 99%

Self – activated segmentation practices of brain tumefaction in MR scan images: a study

Perumal¹,

Devi²,

Rajasekaran³

2021

3C Tecnología

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“…The improved algorithm was used for the simulation of 2D brain deformation. In order to overcome the problems associated with brain atlas to patient registration in the presence of tumors, Diaz and Boulanger [218] proposed a novel method that brings closer the atlas and the patient's image by simulating the mechanical behavior of brain deformation under a tumor pressure. In their study, a meshfree total Lagrangian explicit dynamic (MTLED)…”

Section: Brain Mechanicsmentioning

confidence: 99%

Meshfree and Particle Methods in Biomechanics: Prospects and Challenges

Zhang

Ademiloye

Liew

2018

Arch Computat Methods Eng

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The use of meshfree and particle methods in the field of bioengineering and biomechanics has significantly increased. This may be attributed to their unique abilities to overcome most of the inherent limitations of mesh-based methods in dealing with problems involving large deformation and complex geometry that are common in bioengineering and computational biomechanics in particular. This review article is intended to identify, highlight and summarize research works on topics that are of substantial interest in the field of computational biomechanics in which meshfree or particle methods have been employed for analysis, simulation or/and modeling of biological systems such as soft matters, cells, biological soft and hard tissues and organs. We also anticipate that this review will serve as a useful resource and guide to researchers who intend to extend their work into these research areas. This review article includes 333 references. Highlights  We present an up to date review of meshfree and particle methods, including their advantages and limitations.  We comprehensively discuss past and recent applications of meshfree and particle methods in bioengineering and biomechanics.  We identify research areas, directions, and opportunities for the application of meshfree and particle methods in bioengineering and biomechanics.

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“…In Al‐Shaikhli et al (), the topological graph prior with atlas information is used in a modified multilevel set formulation for multiregion segmentation of brain tumour images. In Diaz and Boulanger (), mesh‐free total Lagrangian explicit dynamic (TLED) method is used to deal simulation with atlas deformation and utilized the shape of the tumour segmented from multimodal MRI to derive a new tumour growth model. This method is able to handle large deformation without remeshing.…”

Section: Brain Tumour Segmentation Techniques Of Mrimentioning

confidence: 99%

Automated brain tumour segmentation techniques— A review

Angulakshmi

Priya

2017

Int J Imaging Syst Tech

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Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article.

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Atlas to patient registration with brain tumor based on a mesh-free method

Cited by 6 publications

References 11 publications

Self – activated segmentation practices of brain tumefaction in MR scan images: a study

Self – activated segmentation practices of brain tumefaction in MR scan images: a study

Meshfree and Particle Methods in Biomechanics: Prospects and Challenges

Automated brain tumour segmentation techniques— A review

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