Suchergebnisse - "Brain segmentation"

Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys

Autoren: Porter, Valerie A Hobson, Brad A D'Almeida, Alita J et al.

Quelle: Computers in Biology and Medicine. 195

Schlagwörter: 46 Information and Computing Sciences (for-2020), 4611 Machine Learning (for-2020), Basic Behavioral and Social Science (rcdc), Dementia (rcdc), Behavioral and Social Science (rcdc), Neurodegenerative (rcdc), Alzheimer's Disease (rcdc), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) (rcdc), Biomedical Imaging (rcdc), Brain Disorders (rcdc), Aging (rcdc), Neurosciences (rcdc), Acquired Cognitive Impairment (rcdc), Bioengineering (rcdc), Neurological (hrcs-hc), Animals (mesh), Magnetic Resonance Imaging (mesh), Brain (mesh), Rats (mesh), Mice (mesh), Imaging, Three-Dimensional (mesh), Neural Networks, Computer (mesh), Disease Models, Animal (mesh), Male (mesh), Alzheimer Disease (mesh), Brain (mesh), Animals (mesh), Mice (mesh), Rats (mesh), Alzheimer Disease (mesh), Disease Models, Animal (mesh), Imaging, Three-Dimensional (mesh), Magnetic Resonance Imaging (mesh), Male (mesh), Neural Networks, Computer (mesh), Brain segmentation, Convolutional neural network, Magnetic resonance imaging, Preclinical models, Transfer learning, Animals (mesh), Magnetic Resonance Imaging (mesh), Brain (mesh), Rats (mesh), Mice (mesh), Imaging, Three-Dimensional (mesh), Neural Networks, Computer (mesh), Disease Models, Animal (mesh), Male (mesh), Alzheimer Disease (mesh), 08 Information and Computing Sciences (for), 09 Engineering (for), 11 Medical and Health Sciences (for), Biomedical Engineering (science-metrix), 3102 Bioinformatics and computational biology (for-2020), 4203 Health services and systems (for-2020), 4601 Applied computing (for-2020)

Zugangs-URL: https://escholarship.org/uc/item/6jd7f9zt

MPRAGElike: A novel approach to generate T1w images from multi‐contrast gradient echo images for brain segmentation

Autoren: Marc‐Antoine Fortin Rüdiger Stirnberg Yannik Völzke et al.

Quelle: Magn Reson Med
Magnetic resonance in medicine 94(1), 134-149 (2025). doi:10.1002/mrm.30453

Schlagwörter: Male, Adult, methods [Image Interpretation, Computer-Assisted], multi‐parameter mapping, Signal-To-Noise Ratio, Sensitivity and Specificity, UHF‐MRI, methods [Magnetic Resonance Imaging], methods [Image Processing, Computer-Assisted], Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Humans, ddc:610, diagnostic imaging [Brain], neuroimaging, Brain, Reproducibility of Results, Image Enhancement, Imaging Methodology, Magnetic Resonance Imaging, brain segmentation, multi‐contrast, Female, methods [Image Enhancement], anatomy & histology [Brain], synthetic MPRAGE, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39902546
https://pub.dzne.de/record/278071

Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions

Autoren: Meshari D. Alanazi Amna Maraoui Imen Werda et al.

Quelle: IEEE Access, Vol 13, Pp 75847-75860 (2025)

Schlagwörter: densely connected convolutions, brain segmentation, magnetic resonance imaging, Electrical engineering. Electronics. Nuclear engineering, U-Net, Bi-directional, convolutional long short-term memory, TK1-9971

Zugangs-URL: https://doaj.org/article/bd1802c5d12b4cffb8b37e6f34fe85c3

Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery

Autoren: Hsing-Hao Ho Huai-Che Yang Wen-Xiang Yang et al.

Quelle: BMC Medical Imaging, Vol 25, Iss 1, Pp 1-8 (2025)

Schlagwörter: Cerebral arteriovenous malformation, Radiation-induced change, Stereotactic radiosurgery, Deep learning, Brain segmentation, Medical technology, R855-855.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2342

Zugangs-URL: https://doaj.org/article/b98a9600f64941ecaad780c4c0618cf9

Evaluation of non-motor symptoms in Parkinson’s disease using multiparametric MRI with the multiplex sequence

Autoren: He Sui Zhanhao Mo Feng Shi et al.

Quelle: Front Aging Neurosci
Frontiers in Aging Neuroscience, Vol 17 (2025)

Schlagwörter: brain segmentation, Parkinson's disease, Neurosciences. Biological psychiatry. Neuropsychiatry, non-motor symptoms, quantitative MRI analysis, early diagnosis, RC321-571, Neuroscience

Zugangs-URL: https://doaj.org/article/9a3b2d69fa5a43299834476989c6ffb7

Deep learning for automated segmentation of brain edema in meningioma after radiosurgery

Autoren: Huai-Che Yang Tzu-Chiang Peng Zhi-Hong Chen et al.

Quelle: BMC Medical Imaging, Vol 25, Iss 1, Pp 1-8 (2025)

Schlagwörter: Meningioma, Brain edema, Gamma knife radiosurgery, Deep learning, Brain segmentation, Medical technology, R855-855.5

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1471-2342

Zugangs-URL: https://doaj.org/article/28f04b01d6cd462facfceb29488ba4b1

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration

Autoren: Srikrishna, Meera Ashton, Nicholas J. Rial, Alexis Moscoso et al.

Quelle: Alzheimers & Dementia. 20(1):629-640

Schlagwörter: Neurology, Neurologi, brain segmentation, cognition, CSF biomarkers, CT, deep learning, dementia, plasma biomarkers, fluid neurofilament light, cerebrospinal-fluid, alzheimers-disease, brain atrophy, imaging biomarkers, mri, plasma, index, segmentation, Neurosciences & Neurology

Zugangs-URL: https://gup.ub.gu.se/publication/331915

A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation

Autoren: Saritha Saladi Karuna Yepuganti Ravikumar Chinthaginjala et al.

Quelle: Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)

Schlagwörter: MRI, Brain segmentation, Tissue classification, Tumor identification, Machine learning, Intuitionistic –FCM, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/30d201da1a744b80bccbcec274faa18f

The Impacts of Enlarged Subarachnoid Space on Brain Growth and Cortex Maturation in Very Preterm Infants

Autoren: Liangbing Wang Yubo Zhuo Fang Lin et al.

Quelle: Diagnostics, Vol 15, Iss 17, p 2206 (2025)

Schlagwörter: enlarged subarachnoid space, preterm infants, brain segmentation, Medicine (General), R5-920

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-4418/15/17/2206; https://doaj.org/toc/2075-4418

Zugangs-URL: https://doaj.org/article/dc176bb21c074a3ea2e2089dfc3c3cee

FAST-AID Brain: Fast and Accurate Segmentation Tool Using Artificial Intelligence Developed for Brain

Autoren: Mostafa Mehdipour Ghazi Mads Nielsen Petersen, Jens et al.

Weitere Verfasser: Mostafa Mehdipour Ghazi Mads Nielsen Petersen, Jens et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783031959103
Mehdipour Ghazi, M & Nielsen, M 2025, FAST-AID Brain : Fast and Accurate Segmentation Tool Using Artificial Intelligence Developed for Brain . in J Petersen & V A Dahl (eds), Image Analysis-23rd Scandinavian Conference, SCIA 2025, Proceedings . Springer, Lecture Notes in Computer Science, vol. 15725 LNCS, pp. 161-176, 23rd Scandinavian Conference on Image Analysis, SCIA 2025, Reykjavik, Iceland, 23/06/2025 . https://doi.org/10.1007/978-3-031-95911-0_12
Mehdipour Ghazi, M & Nielsen, M 2022 ' FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain ' arXiv.org . < https://arxiv.org/abs/2208.14360 >

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, ICV estimation, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, deep learning, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, FOS: Electrical engineering, electronic engineering, information engineering, MRI data augmentation, Brain segmentation, hierarchical Softmax, weakly supervised learning

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2208.14360
https://curis.ku.dk/ws/files/457242092/FAST-AID_Brain.pdf
https://curis.ku.dk/ws/files/339905214/FAST_AID_Brain.pdf

Deep learning for brain electron microscopy segmentation: Advances, challenges, and future directions in connectomics and ultrastructure analysis

Autoren: Uzair Shah Mahmood Alzubaidi Marco Agus et al.

Schlagwörter: Biomedical and clinical sciences, Neurosciences, Health sciences, Health services and systems, Information and computing sciences, Artificial intelligence, Machine learning, Deep learning, Electron microscopy, Brain segmentation, Connectomics, Neural circuits, Convolutional neural networks, Transformers, Meta-analysis

Relation: https://figshare.com/articles/journal_contribution/Deep_learning_for_brain_electron_microscopy_segmentation_Advances_challenges_and_future_directions_in_connectomics_and_ultrastructure_analysis/30135124

Verfügbarkeit: https://doi.org/10.1016/j.cag.2025.104391
https://figshare.com/articles/journal_contribution/Deep_learning_for_brain_electron_microscopy_segmentation_Advances_challenges_and_future_directions_in_connectomics_and_ultrastructure_analysis/30135124

Additional file 4 of Deep learning for automated segmentation of brain edema in meningioma after radiosurgery

Autoren: Huai-Che Yang Tzu-Chiang Peng Zhi-Hong Chen et al.

Schlagwörter: Artificial Intelligence and Image Processing, Meningioma, Brain edema, Gamma knife radiosurgery, Deep learning, Brain segmentation

Relation: https://figshare.com/articles/journal_contribution/Additional_file_4_of_Deep_learning_for_automated_segmentation_of_brain_edema_in_meningioma_after_radiosurgery/28845639

Verfügbarkeit: https://doi.org/10.6084/m9.figshare.28845639.v1
https://figshare.com/articles/journal_contribution/Additional_file_4_of_Deep_learning_for_automated_segmentation_of_brain_edema_in_meningioma_after_radiosurgery/28845639

Additional file 2 of Deep learning for automated segmentation of brain edema in meningioma after radiosurgery

Autoren: Huai-Che Yang Tzu-Chiang Peng Zhi-Hong Chen et al.

Schlagwörter: Artificial Intelligence and Image Processing, Meningioma, Brain edema, Gamma knife radiosurgery, Deep learning, Brain segmentation

Relation: https://figshare.com/articles/journal_contribution/Additional_file_2_of_Deep_learning_for_automated_segmentation_of_brain_edema_in_meningioma_after_radiosurgery/28845633

Verfügbarkeit: https://doi.org/10.6084/m9.figshare.28845633.v1
https://figshare.com/articles/journal_contribution/Additional_file_2_of_Deep_learning_for_automated_segmentation_of_brain_edema_in_meningioma_after_radiosurgery/28845633

Additional file 1 of Deep learning for automated segmentation of brain edema in meningioma after radiosurgery

Autoren: Huai-Che Yang Tzu-Chiang Peng Zhi-Hong Chen et al.

Schlagwörter: Artificial Intelligence and Image Processing, Meningioma, Brain edema, Gamma knife radiosurgery, Deep learning, Brain segmentation

Relation: https://figshare.com/articles/journal_contribution/Additional_file_1_of_Deep_learning_for_automated_segmentation_of_brain_edema_in_meningioma_after_radiosurgery/28845630

Verfügbarkeit: https://doi.org/10.6084/m9.figshare.28845630.v1
https://figshare.com/articles/journal_contribution/Additional_file_1_of_Deep_learning_for_automated_segmentation_of_brain_edema_in_meningioma_after_radiosurgery/28845630

Deep learning applied to the segmentation of rodent brain MRI data outperforms noisy ground truth on full-fledged brain atlases

Autoren: Jonas Kohler Thomas Bielser Stanislaw Adaszewski et al.

Quelle: NeuroImage, Vol 304, Iss , Pp 120934- (2024)

Schlagwörter: Brain segmentation, Rodent MRI, Deep learning, Attention U-Net, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1053811924004312; https://doaj.org/toc/1095-9572

Zugangs-URL: https://doaj.org/article/040c41ba096746febafd8204bbb28d77

Methodological challenges of measuring brain volumes and cortical thickness in idiopathic normal pressure hydrocephalus with a surface-based approach

Autoren: Martina Del Giovane Michael C. B. David Magdalena A. Kolanko et al.

Quelle: Front Neurosci
Frontiers in Neuroscience, Vol 18 (2024)

Schlagwörter: normal pressure hydrocephalus, (NPH), FreeSurfer, enlarged ventricles, Neurosciences. Biological psychiatry. Neuropsychiatry, 3. Good health, brain segmentation, 03 medical and health sciences, 0302 clinical medicine, dementia, RC321-571, Neuroscience

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39099637
https://doaj.org/article/99445b8a50d743b5972fb3cdcfb00404

Evaluating age‐and gender‐related changes in brain volumes in normal adult using synthetic magnetic resonance imaging

Autoren: Zuofeng Zheng Yawen Liu Zhenchang Wang et al.

Quelle: Brain Behav
Brain and Behavior, Vol 14, Iss 7, Pp n/a-n/a (2024)

Schlagwörter: Male, Adult, Aging, synthetic MRI, Neurosciences. Biological psychiatry. Neuropsychiatry, quantitative MRI, Young Adult, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, Reference Values, Humans, Gray Matter, Aged, Sex Characteristics, aging, Age Factors, Brain, Organ Size, Middle Aged, Magnetic Resonance Imaging, White Matter, brain segmentation, Original Article, Female, RC321-571

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38970221
https://doaj.org/article/75fbcba8613745f897eba36e0e87c83b

Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging

Autoren: Fletcher, Evan DeCarli, Charles Fan, Audrey P et al.

Schlagwörter: Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Biomedical Imaging, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Bioengineering, Neurological, Generic health relevance, magnetic resonance imaging, brain segmentation, deep learning, convolutional neural network, medical image processing, medical imaging data ground truth, Cognitive Sciences, Biological psychology

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/4x98c2zq
https://escholarship.org/content/qt4x98c2zq/qt4x98c2zq.pdf

Unraveling Systematic Biases in Brain Segmentation: Insights from Synthetic Training

Autoren: Valabregue, Romain Khemir, Ines Auzias, Guillaume et al.

Weitere Verfasser: Valabregue, Romain Khemir, Ines Auzias, Guillaume et al.

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Bias, Synthetic, Validation, Deep neural network, Brain segmentation

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04608723v1

Model za segmentaciju i analizu morfometrijskih značajki iz MR slika mozga

Autoren: Jarni, Ivana Božek, Jelena

Weitere Verfasser: Jarni, Ivana Božek, Jelena

Schlagwörter: ADNI baza podataka, brain segmentation, FSL, brain MRI, morphometric analysis, MR mozga, Alzheimerova bolest, Alzheimer's disease, morfometrijska analiza, ADNI database, segmentacija mozga

Dateibeschreibung: application/pdf

Zugangs-URL: https://repozitorij.fer.unizg.hr/islandora/object/fer:13846/datastream/PDF
https://repozitorij.fer.unizg.hr/islandora/object/fer:13846
https://urn.nsk.hr/urn:nbn:hr:168:442370