Literatura académica sobre el tema "Cerebrovascular network"
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Artículos de revistas sobre el tema "Cerebrovascular network"
Yu, Qifeng, Yuming Jiao, Ran Huo, Hongyuan Xu, Jie Wang, Shaozhi Zhao, Qiheng He et al. "Application of the concept of neural networks surgery in cerebrovascular disease treatment". Brain & Heart 1, n.º 1 (30 de diciembre de 2022): 223. http://dx.doi.org/10.36922/bh.v1i1.223.
Texto completoMarshall, Olga, Sanjeev Chawla, Hanzhang Lu, Louise Pape y Yulin Ge. "Cerebral blood flow modulation insufficiency in brain networks in multiple sclerosis: A hypercapnia MRI study". Journal of Cerebral Blood Flow & Metabolism 36, n.º 12 (20 de julio de 2016): 2087–95. http://dx.doi.org/10.1177/0271678x16654922.
Texto completoYang, Zhengfei, Ping Li y Rui Wang. "Prediction of Metabolic Characteristics of Cardiovascular and Cerebrovascular Diseases Based on Convolutional Neural Network". Computational and Mathematical Methods in Medicine 2022 (27 de julio de 2022): 1–13. http://dx.doi.org/10.1155/2022/3206378.
Texto completoTay, Jonathan, Danuta M. Lisiecka-Ford, Matthew J. Hollocks, Anil M. Tuladhar, Thomas R. Barrick, Anne Forster, Michael J. O’Sullivan et al. "Network neuroscience of apathy in cerebrovascular disease". Progress in Neurobiology 188 (mayo de 2020): 101785. http://dx.doi.org/10.1016/j.pneurobio.2020.101785.
Texto completoLiu, Hanqing, Xiaojun Li, Jin Wei y Xiaodong Kang. "Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm". Discrete Dynamics in Nature and Society 2022 (26 de abril de 2022): 1–12. http://dx.doi.org/10.1155/2022/4494411.
Texto completoLiu, Hanqing, Xiaojun Li, Jin Wei y Xiaodong Kang. "Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm". Discrete Dynamics in Nature and Society 2022 (26 de abril de 2022): 1–12. http://dx.doi.org/10.1155/2022/4494411.
Texto completoQin, Qiuli, Xing Yang, Runtong Zhang, Manlu Liu y Yuhan Ma. "An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk". Journal of Organizational and End User Computing 34, n.º 4 (julio de 2022): 1–14. http://dx.doi.org/10.4018/joeuc.287574.
Texto completoLin, Wei-Wei, Lin-Tao Xu, Yi-Sheng Chen, Ken Go, Chenyu Sun y Yong-Jian Zhu. "Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature". BioMed Research International 2021 (23 de julio de 2021): 1–15. http://dx.doi.org/10.1155/2021/7643209.
Texto completoCabrera DeBuc, Delia, Gabor Mark Somfai y Akos Koller. "Retinal microvascular network alterations: potential biomarkers of cerebrovascular and neural diseases". American Journal of Physiology-Heart and Circulatory Physiology 312, n.º 2 (1 de febrero de 2017): H201—H212. http://dx.doi.org/10.1152/ajpheart.00201.2016.
Texto completoLiu, Yongwei, Hyo-Sung Kwak y Il-Seok Oh. "Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs". Applied Sciences 12, n.º 5 (22 de febrero de 2022): 2288. http://dx.doi.org/10.3390/app12052288.
Texto completoTesis sobre el tema "Cerebrovascular network"
Åström, Monica. "Depression after stroke". Doctoral thesis, Umeå universitet, Psykiatri, 1993. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-96912.
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digitalisering@umu
Rougé, Pierre. "Segmentation et modélisation du réseau vasculaire cérébral à partir d'images IRM". Electronic Thesis or Diss., Reims, 2025. http://www.theses.fr/2025REIMS001.
Texto completoCardio-neurovascular diseases are the leading cause of death worldwide and represent a major public health challenge. Imaging of the cerebral vascular network has significantly improved the diagnosis of these pathologies, and automated image processing algorithms now play a key role in assisting physicians. These algorithms generally rely on the segmentation of the cerebral vascular network. For this reason, automating this task has garnered significant interest.Despite advances, current automatic segmentation methods still suffer from major limitations. They struggle to preserve the topology and connectivity of vascular networks, and traditional segmentation metrics are not well-suited to the geometric complexity of the cerebrovascular network. Additionally, manual annotation, necessary for training these models, remains a time-consuming and tedious task, hindering the creation of annotated datasets.In this thesis, we focus on cerebrovascular segmentation from TOF MRA images. First, we propose a multitask model based on a topological cost function to improve the connectivity of segmentations. Additionally, we introduce a new metric, called ccDice, to quantify topological errors. Finally, we study the impact of annotation scarcity and noise, and we formulate recommendations for clinicians to improve annotation quality, thereby fostering the development of more efficient learning models in the future
Kleineibst, Lynn Jill. "The effectiveness of a caregiver support programme to address the needs of primary caregivers of stroke patients in a low socio economic community". Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/432.
Texto completoMendes, Luciana Moura. "Modelo de apoio à decisão no acesso aos serviços de fisioterapia para reabilitação de pacientes com acidente vascular encefálico". Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/7551.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Cerebrovascular Accident (CVA) is a disease characterized by an interruption of blood flow to the encephalon, which represents the leading cause of long-term disability and functional impairment in adult population. Therefore, the individual who had suffered CVA needs to access health services that offer rehabilitation assistance as they promote a better physical, functional, and mental capacity, helping the reinsertion and reintegration of this individual into society. Thus, this study aims to develop a decision-making model to determine the access to physiotherapy services for rehabilitation of patients who had suffered acute CVA in the cities of João Pessoa and Cabedelo. This is an observational-longitudinal study among man and women who were admitted at a public hospital in João Pessoa and live in its metro area, who had presented CVA as primary cause of hospitalization. A questionnaire was used containing items related to socioeconomic, demographic, and clinical data from this person, such as general health conditions, risk factors, functionality evaluation, and access to physiotherapy services. Interviews were conducted between 15 and 21 days after hospital discharge (T0) and between 90 and 105 days after the first interview (T1). There was a homogeneous distribution of sexes, group age over 60 years (mean age= 61.6 years; standard deviation= 15.7 years). Most of the subjects (69.2%) have had a ischemic CVA, which the right side was more affected (46.2%) and 89.7% have had up to two CVA episodes. From interviewed patients, 69.2% have not had access to physiotherapy services after three months from the first interview. For utilization of decision model, 16 variables were selected helped by WEKA software, generating a feedfoward Artificial Neural Network model composed by 16 neurons in the input layer, followed by two hidden layers with two hidden neurons in each layer and an output layer with 2 neurons with backpropagation learning. This decision model allowed classifying correctly almost all subjects that accessed or not the physiotherapy services, achieving 97.4% of successes, representing a greater reliability. Therefore, this model is constituted as an important tool in the visibility of the problem, helping in the decision-making process, planning, and reorganization of public health system and its several attention levels.
O Acidente Vascular Encefálico (AVE) é uma doença causada pela interrupção no suprimento sanguíneo ao encéfalo, representando a primeira causa de incapacidade prolongada e o comprometimento funcional em adultos. Assim, o indivíduo com AVE necessita acessar os serviços de saúde que oferecem assistência de reabilitação, pois promovem uma melhora na capacidade física, funcional e/ou mental, proporcionando a reinserção e a reintegração à sociedade. Portanto, o objetivo deste estudo foi elaborar um modelo de tomada de decisão para averiguar o acesso aos serviços de fisioterapia para reabilitação de pacientes com AVE agudo dos municípios de João Pessoa e Cabedelo. Trata-se de um estudo longitudinal observacional com indivíduos de ambos os sexos, admitidos em um hospital público de João Pessoa/PB e residentes na região metropolitana de João Pessoa, que apresentaram como causa primária da internação o AVE. Para tanto, foi utilizado um questionário contendo itens referentes aos dados socioeconômicos, demográficos e clínicos do sujeito, condições gerais de saúde, fatores de risco, avaliação da funcionalidade e do acesso aos serviços de fisioterapia. As entrevistas foram realizadas entre 15 e 21 dias após a alta hospitalar (T0) e entre 90 e 105 dias após a realização da primeira entrevista (T1). Verificou-se uma distribuição homogênea dos sexos, com faixa etária acima de 60 anos (média de idade=61,6 anos, dp=15,7). A maioria dos sujeitos (69,2%) tiveram um AVE do tipo isquêmico, sendo o lado direito mais afetado (46,2%) e 89,7% tiveram até dois episódios de AVE. Dos pacientes entrevistados, 69,2% não tiveram acesso aos serviços de fisioterapia após três meses da primeira entrevista. Para a utilização do modelo de decisão, selecionou-se 16 variáveis com auxílio do software WEKA, gerando um modelo de Redes Neurais Artificiais do tipo feedforward composta por 16 neurônios na camada de entrada, seguido por duas camadas ocultas com dois neurônios ocultos em cada e uma camada de saída com 2 neurônios com aprendizagem por backpropagation. Este modelo de decisão permitiu classificar corretamente quase todos os sujeitos que acessaram ou não os serviços de fisioterapia, obtendo 97,4% de acertos, representando uma maior confiabilidade. Portanto, este modelo constitui-se como uma ferramenta importante na visibilidade do problema, auxiliando no processo de tomada de decisão, no planejamento e na reorganização da rede de saúde em seus diversos níveis de atenção.
Earnheart, Kristie. "Cardiovascular Problems as a Predictor of Later Cognitive Decline: Moderating Effect of General and Spousal Social Support". Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5377/.
Texto completoLibros sobre el tema "Cerebrovascular network"
Publications, ICON Health. Cerebral Vascular Accident: A Medical Dictionary, Bibliography, And Annotated Research Guide To Internet References. Icon Health Publications, 2004.
Buscar texto completoCapítulos de libros sobre el tema "Cerebrovascular network"
Zhang, Hao, Likun Xia, Ran Song, Jianlong Yang, Huaying Hao, Jiang Liu y Yitian Zhao. "Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 66–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59725-2_7.
Texto completoYang, Chaozhi, Yachuan Li, Yun Bai, Qian Xiao, Zongmin Li, Hongyi Li y Hua Li. "SS-Net: 3D Spatial-Spectral Network for Cerebrovascular Segmentation in TOF-MRA". En Artificial Neural Networks and Machine Learning – ICANN 2023, 149–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44213-1_13.
Texto completoXie, Qihang, Dan Zhang, Lei Mou, Shanshan Wang, Yitian Zhao, Mengguo Guo y Jiong Zhang. "DSNet: A Spatio-Temporal Consistency Network for Cerebrovascular Segmentation in Digital Subtraction Angiography Sequences". En Lecture Notes in Computer Science, 199–208. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72111-3_19.
Texto completoQin, Qiuli, Chunxiao Yao y Yong Jiang. "Research on Cerebrovascular Disease Prediction Model Based on the Long Short Term Memory Neural Network". En Smart Health, 247–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34482-5_22.
Texto completoWang, Yifan, Guoli Yan, Haikuan Zhu, Sagar Buch, Ying Wang, Ewart Mark Haacke, Jing Hua y Zichun Zhong. "JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 106–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59725-2_11.
Texto completoRazumnikova, Olga y Vladislav Kagan. "Aging Associated Specificity in Training Visual Short-Term Memory". En Cerebrovascular Diseases [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.101669.
Texto completoForkert Nils Daniel, Suniaga Santiago, Fiehler Jens, Wersching Heike, Knecht Stefan y Kemmling Andre. "Generation of a Probabilistic Arterial Cerebrovascular Atlas Derived from 700 Time-of-Flight MRA Datasets". En Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-101-4-148.
Texto completoCoelho Silva, Henrique, Rafael Costa Lima Maia, Paulo Roberto Leitao de Vasconcelos y Orleancio Gomes Ripardo de Azevedo. "The Pathophysiological Aspects of Cerebral Diseases". En Cerebrovascular Diseases [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101218.
Texto completoGuozheng Qian, Youfa Li, Guiqing Wang y Yifeng Cao. "Studies on Improved Model for Cerebrovascular Blood Circulation". En Studies in Health Technology and Informatics. IOS Press, 2001. https://doi.org/10.3233/978-1-60750-928-8-1339.
Texto completoMarchi, Nicola y Amy L. Brewster. "Pericytes and Microglia". En Jasper's Basic Mechanisms of the Epilepsies, editado por Annamaria Vezzani y Helen E. Scharfman, 589–610. 5a ed. Oxford University PressNew York, 2024. http://dx.doi.org/10.1093/med/9780197549469.003.0029.
Texto completoActas de conferencias sobre el tema "Cerebrovascular network"
Shan, Wenqi, Qiang Li y Zhiwei Wang. "SPNet: Sparse-mask Prompt-learning Network for Cerebrovascular Segmentation". En ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889326.
Texto completoSanchesa, Pedro, Cyril Meyer, Vincent Vigon y Benoit Naegel. "Cerebrovascular Network Segmentation of MRA Images With Deep Learning". En 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759569.
Texto completoYan, Songlin, Weijing Xu, Wentao Liu, Huihua Yang, Lemeng Wang, Yiming Deng y Feng Gao. "TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation". En 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2023. http://dx.doi.org/10.1109/embc40787.2023.10340540.
Texto completoDu, Chencheng, Ping'an Li y Kehao Wang. "An automatic extraction method of cerebrovascular centerline for MRA". En 2016 5th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2016. http://dx.doi.org/10.1109/iccsnt.2016.8070254.
Texto completoWu, Qian, Yufei Chen, Ning Huang y Xiaodong Yue. "Weakly-supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator". En ICMR '22: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3512527.3531377.
Texto completoFiona Mary, M., M. Rajeswari y M. Amalasweena. "Neural Network-based Prognostic Model for Cerebrovascular Accident using CT Scans". En 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104728.
Texto completoMalykhina, Galina, Vyacheslav Salnikov, Vladimir Semenyutin y Dmitriy Tarkhov. "Digitalization of medical services for detecting violations of cerebrovascular regulation based on a neural network signal analysis algorithm". En SPBPU IDE '20: SPBPU IDE-2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3444465.3444526.
Texto completoPacheco Pachado, Mayra, Alexandra Petraina, Cristian Nogales, Theodora Saridaki, Harald H. H. W. Schmidt y Ana I. Casas. "An organ-agnostic drug repurposing strategy for dementia: Pre-clinical validation of network pharmacology to treat cerebrovascular dysfunction and cognitive impairment". En RExPO22. ScienceOpen, 2022. http://dx.doi.org/10.14293/s2199-1006.1.sor-.ppplken3.v1.
Texto completoTokuda, Shigefumi, Takeshi Unemura y Marie Oshima. "Computational Study on the Effects of Peripheral Vessel Network on Blood Flow in the Arterial Circle of Willis". En ASME 2007 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2007. http://dx.doi.org/10.1115/sbc2007-176706.
Texto completoFaes, Luca, Gorana Mijatovic, Laura Sparacino, Riccardo Pernice, Yuri Antonacci, Alberto Porta y Sebastiano Stramaglia. "Quantifying High-Order Interactions in Cardiovascular and Cerebrovascular Networks". En 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO). IEEE, 2022. http://dx.doi.org/10.1109/esgco55423.2022.9931385.
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