Treelike neural network for brain magnetic resonance image segmentation

1997 ◽  
Author(s):  
Iren Valova ◽  
Yukio Kosugi
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Brain Magnetic Resonance Image

scholarly journals Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Ryo Sakamoto ◽  
Christopher Marano ◽  
Michael I. Miller ◽  
Constantine G. Lyketsos ◽  
Yue Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Prediction Model ◽  
Magnetic Resonance Image ◽  
Training Dataset ◽  
Clinical Use ◽  
Care Providers ◽  
Edge Image ◽  
Number Of Patients ◽  
Brain Magnetic Resonance Image

For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers. Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making. To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud. The model was built in a training dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) where baseline MRI scans were combined with clinical data over time. Each MRI was parcellated into 265 anatomical units based on the MRICloud fully automated image segmentation function, to measure the volume of each parcel. The Mini Mental State Examination (MMSE) was used as a measure of cognitive function. The normalized volume of 265 parcels, combined with baseline MMSE score, age, and sex were input variables for a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with MMSE change in the subsequent two years as the target for prediction. A leave-one-out analysis performed on the training dataset estimated a correlation coefficient of 0.64 between true and predicted MMSE change. A receiver operating characteristic (ROC) analysis estimated a sensitivity of 0.88 and a specificity of 0.76 in predicting substantial cognitive worsening after two years, defined as MMSE decline of ≥4 points. This MRICloud prediction model was then applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer’s Treatment Center (MATC), a clinical care setting. In the latter setting, the model had both sensitivity and specificity of 1.0 in predicting substantial cognitive worsening. While the MRICloud prediction model demonstrated promise as a platform on which computational MRI findings can easily be extended to clinical use, further study with a larger number of patients is needed for validation.

scholarly journals An active contour model for brain magnetic resonance image segmentation based on multiple descriptors

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878341 ◽  
Author(s):  
Chen Hong ◽  
Yu Xiaosheng ◽  
Wu Chengdong ◽  
Wu Jiahui
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Tissue ◽  
Magnetic Resonance Image ◽  
Active Contour ◽  
Active Contour Model ◽  
Full Description ◽  
Contour Model ◽  
Brain Magnetic Resonance Image ◽  
The Brain

With the increasing use of surgical robots, robust and accurate segmentation techniques for brain tissue in the brain magnetic resonance image are needed to be embedded in the robot vision module. However, the brain magnetic resonance image segmentation results are often unsatisfactory because of noise and intensity inhomogeneity. To obtain accurate segmentation of brain tissue, one new multiphase active contour model, which is based on multiple descriptors mean, variance, and the local entropy, is proposed in this study. The model can bring about a more full description of local intensity distribution. Also, the entropy is introduced to improve the performance of robustness to noise of the algorithm. The segmentation and bias correction for brain magnetic resonance image can be simultaneously incorporated by introducing the bias factor in the proposed approach. At last, three experiments are carried out to test the performance of the method. The results in the experiments show that method proposed in this study performed better than most current methods in regard to accuracy and robustness. In addition, the bias-corrected images obtained by proposed method have better visual effect.

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