A Multi-scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients

2021 ◽  
pp. 241-252
Author(s):  
Naresh Nandakumar ◽  
Komal Manzoor ◽  
Shruti Agarwal ◽  
Jay J. Pillai ◽  
Sachin K. Gujar ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Temporal Attention ◽  
Tumor Patients ◽  
Attention Network ◽  
Multi Scale ◽  
Eloquent Cortex ◽  
Dynamic Connectivity

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals Automated Eloquent Cortex Localization in Brain Tumor Patients Using Multi-task Graph Neural Networks

2021 ◽  
pp. 102203
Author(s):  
Naresh Nandakumar ◽  
Komal Manzoor ◽  
Shruti Agarwal ◽  
Jay J. Pillai ◽  
Sachin K. Gujar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Task Graph ◽  
Tumor Patients ◽  
Eloquent Cortex ◽  
Graph Neural Networks

scholarly journals Genetic variants and cognitive functions in patients with brain tumors

2019 ◽  
Vol 21 (10) ◽  
pp. 1297-1309 ◽  
Author(s):  
Denise D Correa ◽  
Jaya Satagopan ◽  
Axel Martin ◽  
Erica Braun ◽  
Maria Kryza-Lacombe ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Cell Cycle ◽  
Dna Repair ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Cell Cycle Regulation ◽  
Cognitive Outcome ◽  
Cholesterol Transport ◽  
Tumor Patients ◽  
Cycle Regulation

AbstractBackgroundPatients with brain tumors treated with radiotherapy (RT) and chemotherapy (CT) often experience cognitive dysfunction. We reported that single nucleotide polymorphisms (SNPs) in the APOE, COMT, and BDNF genes may influence cognition in brain tumor patients. In this study, we assessed whether genes associated with late-onset Alzheimer’s disease (LOAD), inflammation, cholesterol transport, dopamine and myelin regulation, and DNA repair may influence cognitive outcome in this population.MethodsOne hundred and fifty brain tumor patients treated with RT ± CT or CT alone completed a neurocognitive assessment and provided a blood sample for genotyping. We genotyped genes/SNPs in these pathways: (i) LOAD risk/inflammation/cholesterol transport, (ii) dopamine regulation, (iii) myelin regulation, (iv) DNA repair, (v) blood–brain barrier disruption, (vi) cell cycle regulation, and (vii) response to oxidative stress. White matter (WM) abnormalities were rated on brain MRIs.ResultsMultivariable linear regression analysis with Bayesian shrinkage estimation of SNP effects, adjusting for relevant demographic, disease, and treatment variables, indicated strong associations (posterior association summary [PAS] ≥ 0.95) among tests of attention, executive functions, and memory and 33 SNPs in genes involved in: LOAD/inflammation/cholesterol transport (eg, PDE7A, IL-6), dopamine regulation (eg, DRD1, COMT), myelin repair (eg, TCF4), DNA repair (eg, RAD51), cell cycle regulation (eg, SESN1), and response to oxidative stress (eg, GSTP1). The SNPs were not significantly associated with WM abnormalities.ConclusionThis novel study suggests that polymorphisms in genes involved in aging and inflammation, dopamine, myelin and cell cycle regulation, and DNA repair and response to oxidative stress may be associated with cognitive outcome in patients with brain tumors.

scholarly journals Automatic lumbar spinal MRI image segmentation with a multi-scale attention network

2021 ◽  
Author(s):  
Haixing Li ◽  
Haibo Luo ◽  
Wang Huan ◽  
Zelin Shi ◽  
Chongnan Yan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Spinal Mri ◽  
Lumbar Spinal ◽  
Mri Image
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