Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

Kalavathi, P.; Senthamilselvi, K.; Prasath, V. B. Surya

doi:10.20944/preprints201611.0138.v1

Cited by 9 publications

(10 citation statements)

References 39 publications

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“…Modern medical imaging techniques have given rise to the development of methodologies for the construction of data analysis systems for medical applications such as lesion segmentation and diseases diagnosis . Such systems uses information extracted from medical images data sets obtained by computed tomography (CT) or magnetic resonance imaging (MRI), providing surgeons with tools for better decision making.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

Villanueva-Naquid

Soubervielle‐Montalvo

Aguilar-Ponce

et al. 2020

Robotics Computer Surgery

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Background: Preoperative assessment to find the safest trajectory in keyhole neurosurgery can reduce post operative complications. Methods: We introduced a novel preoperative risk assessment semiautomated methodology based on the sum of N maximum risk values using a generic genetic algorithm for the safest trajectory search. Results: A set of candidates trajectories were found for two surgical procedures. The trajectories search is done using a risk map considering the proximity of voxels within risk structures in multiple points and a genetic algorithm to avoid an exhaustive search. The trajectories were validated by a group of neurosurgeons. Conclusions: The trajectories obtained with the proposal method were shorter in 5% and have greater distance from the voxels within the blood vessels in 4.7%. The use of genetic algorithm (GA) speeds up the search for the safest trajectory, decreasing in 99.9% the time required for an exhaustive search.

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Section: Introductionmentioning

confidence: 99%

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

Villanueva-Naquid

Soubervielle‐Montalvo

Aguilar-Ponce

et al. 2020

Robotics Computer Surgery

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“…In general, the present methods used for MSP detection follow either a feature-based approach or a symmetry-based approach [19]. In the first type of approaches, the aim is to directly determine the inter-hemispheric plane from its intensity and textural features.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Extraction Midsagittal Plane of Skull From Brain CT Images for Oral and Maxillofacial Surgery

et al. 2019

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To prepare for the oral and maxillofacial surgery for the facial symmetry of patients, midsagittal plane of skull in brain computed tomography (CT) images is calculated with points manually chosen from skull by doctor. But the extracted midsagittal plane of the skull is different by different doctor. Even the extracted midsagittal plane of the same patient is also different by the same doctor in different times. The manually extracting operation usually takes a long time to increase the doctor's workload. Aimed at this problem, a semi-automatic extracting method for midsagittal plane of skull is proposed in this paper. First, the brain tissue is extracted by region growing method and the oriented bounding box (OBB) of the brain tissue is built. Second, the middle symmetry plane of the OBB of brain tissue is extracted as the initial midsagittal plane, which is updated by the mathematical translation and rotation method. Finally, the symmetrical characteristic of the brain tissue based on the updated symmetry plane is calculated by the mutual information method. This procedure is executed iteratively until the symmetrical characteristic of the brain tissue based on the new symmetry plane is no more different from the previous result. The final extracted symmetry plane is the midsagittal plane of skull in brain CT images of the patient. The midsagittal plane which is extracted manually by doctor is used to compare and evaluate the accuracy of this semi-automatic extracting symmetry plane method. The experimental results from both qualitative and quantitative analyses showed that the method can reach or approach the accuracy of manual extracted, but the stable level of this method is significantly higher than the manual method and this method can shorten the operate time to reduce the doctor's workload. INDEX TERMS Oral and maxillofacial surgery, head symmetry plane, oriented bounding box (OBB), mutual information.

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confidence: 99%

Deep learning based computer-aided diagnosis for neuroimaging data: focused review and future potential

Prasath¹

2018

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Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience [1][2][3][4] . Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data [5][6][7][8] .Many of the impressive results obtained in CAD using deep learning (DL) techniques utilize mainly image datasets. DL networks typically require annotations of several images for employing supervised learning techniques and are one of the roadblocks in employing these state of the art networks in various classification tasks in MRI/fMRI. However, unsupervised learning techniques within the DL paradigm are now being employed in natural image classification with a lot of success and we believe the adaptability of these to the neuroimaging data are required to attack challenging neuroimage analysis problems.A stacked denoising auto encoders approach that is an unsupervised learning technique was used [9] for brain tumor segmentation in MRI imagery. The experimental results showed that using this particular approach is as good as using supervised learning based DL techniques that require accurate image-based annotations. This indicates that we can use different unsupervised learning in DL networks variants for

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Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

Cited by 9 publications

References 39 publications

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

Risk assessment methodology for trajectory planning in keyhole neurosurgery using genetic algorithms

An Approach to Extraction Midsagittal Plane of Skull From Brain CT Images for Oral and Maxillofacial Surgery

Deep learning based computer-aided diagnosis for neuroimaging data: focused review and future potential

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