Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study: a phantom study

Συγγραφείς: Hajianfar, Ghasem, Hosseini, Seyyed Ali, Bagherieh, Sara, Oveisi, Mehrdad, Shiri Lord, Isaac, Zaidi, Habib

Πηγή: Med Biol Eng Comput
Hajianfar, G, Hosseini, S A, Bagherieh, S, Oveisi, M, Shiri, I & Zaidi, H 2024, 'Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques : a phantom study', Medical and Biological Engineering and Computing, vol. 62, no. 8, pp. 2319-2332. https://doi.org/10.1007/s11517-024-03071-6

Θεματικοί όροι: Scanner effect, Radiomics, 616.0757, Image Processing, Computer-Assisted/methods, Phantoms, Imaging, Image Processing, 4. Education, Reproducibility of Results, Pre-processing, Magnetic Resonance Imaging, Phantoms, Imaging, Magnetic Resonance Imaging/methods, Harmonization, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Original Article, Robustness, 10. No inequality, Algorithms, MRI

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/38536580
https://research.rug.nl/en/publications/071c8fca-484d-4d79-b0eb-e1d9149f4449
https://doi.org/10.1007/s11517-024-03071-6
https://hdl.handle.net/11370/071c8fca-484d-4d79-b0eb-e1d9149f4449

Near-real-time Mueller polarimetric image processing for neurosurgical intervention

Συγγραφείς: Stefano Moriconi, Omar Rodríguez-Núñez, Éléa Gros, Leonard A. Felger, Theoni Maragkou, Ekkehard Hewer, Angelo Pierangelo, Tatiana Novikova, Philippe Schucht, Richard McKinley

Πηγή: Int J Comput Assist Radiol Surg
International journal of computer assisted radiology and surgery, vol. 19, no. 6, pp. 1033-1043

Θεματικοί όροι: Surgery, Computer-Assisted, Brain Neoplasms, Humans, Neurosurgical Procedures/methods, Image Processing, Computer-Assisted/methods, Brain Neoplasms/surgery, Brain Neoplasms/diagnostic imaging, Surgery, Computer-Assisted/methods, White Matter/diagnostic imaging, White Matter/surgery, AI, Mueller polarimetric imaging, Neurosurgery, Real-time denoising, Image and Video Processing (eess.IV), Image Processing, Computer-Assisted, FOS: Electrical engineering, electronic engineering, information engineering, Original Article, Electrical Engineering and Systems Science - Image and Video Processing, White Matter, Neurosurgical Procedures, 3. Good health

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/38503943
http://arxiv.org/abs/2403.00893
http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_3293FB4746117
https://serval.unil.ch/resource/serval:BIB_3293FB474611.P001/REF.pdf
https://serval.unil.ch/notice/serval:BIB_3293FB474611

CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques

Συγγραφείς: Fong Amaris, W M, Suárez, Daniel R, Cortés-Cortés, Liliana J, MARTINEZ LUNA, Carol

Πηγή: Malar J
Malaria Journal, Vol 23, Iss 1, Pp 1-12 (2024)
Malaria Journal, 23 (1), 299 (2024-10-07)

Θεματικοί όροι: Malaria diagnosis, Image Processing, Computer-Assisted/methods, Research, RC955-962, Color, Infectious and parasitic diseases, RC109-216, Thick blood smears, Engineering, computing & technology, Ingénierie, informatique & technologie, Malaria, Machine Learning, Infectious Diseases, Coloration quality, Image processing, Arctic medicine. Tropical medicine, Machine learning, Image Processing, Computer-Assisted, Humans, Parasitology

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/39375756
https://doaj.org/article/37644f44842d459798cfc79192f5b0ff
https://orbilu.uni.lu/bitstream/10993/63458/1/s12936-024-05025-7.pdf
https://orbilu.uni.lu/handle/10993/63458

Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model

Συγγραφείς: Salimi, Yazdan, Mansouri, Zahra, Sun, Chang, Sanaat, Amirhossein, Yazdanpanah, Mohammadhossein, Shooli, Hossein, Nkoulou, René, Boudabbous, Sana, Zaidi, Habib

Πηγή: Radiol Med
Salimi, Y, Mansouri, Z, Sun, C, Sanaat, A, Yazdanpanah, M, Shooli, H, Nkoulou, R, Boudabbous, S & Zaidi, H 2025, 'Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model', La Radiologia Medica, vol. 130, no. 5, pp. 723-739. https://doi.org/10.1007/s11547-025-01989-x

Θεματικοί όροι: Image Processing, Computer-Assisted/methods, Image Processing, Tomography, X-Ray Computed/methods, NnU-Net, Radiation Dosage, Computed Tomography, Deep Learning, Radiographic Image Interpretation, Computer-Assisted/methods, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Organ segmentation, Tomography, Radiographic Image Interpretation, Computer-Assisted / methods, 616.0757, nnU-Net, Radiation dose, Radiographic Image Interpretation, Deep learning, X-Ray Computed/methods, Tomography, X-Ray Computed / methods, Ultra-low-dose CT, Image Processing, Computer-Assisted / methods, Radiographic Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, Algorithms

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/40100539

Multiple teachers‐meticulous student: A domain adaptive meta‐knowledge distillation model for medical image classification: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

Συγγραφείς: Nabavi, Shahabedin, Anvari Hamedani, Kian, Ebrahimi Moghaddam, Mohsen, Abin, Ahmad Ali, Frangi, Alejandro F

Πηγή: Nabavi, S, Anvari Hamedani, K, Ebrahimi Moghaddam, M, Abin, A A & Frangi, A F 2026, 'Multiple teachers-meticulous student : A domain adaptive meta-knowledge distillation model for medical image classification', Medical Physics, vol. 53, no. 2, pp. e70350. https://doi.org/10.1002/mp.70350
Nabavi, S, Hamedani, K A, Moghaddam, M E, Abin, A A & Frangi, A F 2024 'Multiple Teachers-Meticulous Student : A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification' arXiv. https://doi.org/10.48550/arXiv.2403.11226
Nabavi, S, Anvari Hamedani, K, Ebrahimi Moghaddam, M, Abin, A A & Frangi, A F 2026, 'Multiple teachers-meticulous student : A domain adaptive meta-knowledge distillation model for medical image classification', Medical Physics, vol. 53, no. 2, e70350. https://doi.org/10.1002/mp.70350

Θεματικοί όροι: Domain adaptation, Diagnostic Imaging, Image Processing, Computer-Assisted/methods, domain adaptation, Image and Video Processing (eess.IV), deep learning, Deep learning, Image and Video Processing, Knowledge distillation, teacher-student model, Teacher-student model, Deep Learning, Knowledge, knowledge distillation, FOS: Electrical engineering, electronic engineering, information engineering, Medical image classification, Humans, Students, medical image classification

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: http://arxiv.org/abs/2403.11226
https://research.manchester.ac.uk/en/publications/58aef418-a338-4317-afd1-c5cbb249ba54
https://doi.org/10.1002/mp.70350
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_starter&SrcAuth=WosAPI&KeyUT=WOS:001692229300001&DestLinkType=FullRecord&DestApp=WOS_CPL
https://research.manchester.ac.uk/en/publications/6206fdd7-81f7-48df-a865-615c7c0016eb
https://doi.org/10.1002/mp.70350

A noise-robust post-processing pipeline for accelerated phase-cycled 23Na Multi-Quantum Coherences MRI

Συγγραφείς: Licht, Christian, Ilicak, Efe, Boada, Fernando, Guye, Maxime, Zöllner, Frank, Schad, Lothar, Rapacchi, Stanislas

Συνεισφορές: Kober, Frank

Πηγή: Z Med Phys
Zeitschrift fur medizinische Physik, vol. 35, no. 1, pp. 98-108

Θεματικοί όροι: Original Paper, Single and triple quantum imaging, Sodium, Brain, Neuroimaging, Signal-To-Noise Ratio, Magnetic Resonance Imaging, Sodium multi-quantum coherences, Low-rank, Dynamic mode decomposition, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Sodium MRI, Image Processing, Computer-Assisted, Humans, Magnetic Resonance Imaging/methods, Brain/diagnostic imaging, Algorithms, Image Processing, Computer-Assisted/methods, Sodium Isotopes

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/39843306
https://serval.unil.ch/resource/serval:BIB_27DCDCBE9483.P001/REF.pdf
http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_27DCDCBE94835
https://serval.unil.ch/notice/serval:BIB_27DCDCBE9483
https://amu.hal.science/hal-04998085v1/document
https://doi.org/10.1016/j.zemedi.2024.12.004
https://amu.hal.science/hal-04998085v1

Development of image analysis tool to evaluate Langerhans cell migration after exposure to isothiazolinones

Συγγραφείς: Oltramare, C, Burri, O, Hopf, NB, Jaccoud, S, Seitz, A, Applegate, LA, Hirt-Burri, N, Berthet, A

Πηγή: Arch Toxicol
Archives of toxicology, vol. 99, no. 6, pp. 2463-2477

Θεματικοί όροι: Machine Learning, Thiazoles, Immunotoxicology, Cell Movement, Langerhans Cells, Langerhans Cells/drug effects, Humans, Cell Movement/drug effects, Thiazoles/toxicity, Dermatitis, Allergic Contact/etiology, Dermatitis, Allergic Contact/pathology, Skin/drug effects, Skin/cytology, Image Processing, Computer-Assisted/methods, Software, Allergic contact dermatitis, DAB-staining CD1a immunohistochemistry, In vitro model, QuPath software, Skin histology, Skin sensitization, Dermatitis, Allergic Contact, Image Processing, Computer-Assisted, Skin

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/40080107
http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_F482B4531F1F7
https://serval.unil.ch/notice/serval:BIB_F482B4531F1F
https://serval.unil.ch/resource/serval:BIB_F482B4531F1F.P001/REF.pdf

Uncertainty CNNs: A path to enhanced medical image classification performance

Συγγραφείς: Papageorgiou, Vasileios E, Petmezas, Georgios, DOGOULIS, Panteleimon Tsampikos, CORDY, Maxime, Maglaveras, Nicos

Πηγή: Mathematical Biosciences and Engineering, Vol 22, Iss 3, Pp 528-553 (2025)
Mathematical Biosciences and Engineering, 22 (3), 528-553 (2025-02-20)

Θεματικοί όροι: Test sets, Sciences informatiques, Diagnostic Imaging, Neoplasms/diagnostic imaging, Classification performance, Lung Neoplasms, Image Processing, Computer-Assisted/methods, uncertainty quantification, Convolutional neural network, Lung Neoplasms/diagnostic imaging, Ingénierie, informatique & technologie, Deep Learning, Neoplasms, convolutional neural networks, Image Interpretation, Computer-Assisted, QA1-939, Image Processing, Computer-Assisted, Humans, Tumour detection, Uncertainty quantifications, tumor detection, Biomedical images, Heart Failure, Applied Mathematics, Image Interpretation, Computer-Assisted/methods, Uncertainty, Images classification, biomedical image classification, artificial intelligence, Computer science, Magnetic Resonance Imaging, Engineering, computing & technology, Heart Failure/diagnostic imaging, Computational Mathematics, Agricultural and Biological Sciences (all), Modeling and Simulation, Neural Networks, Computer, test-set augmentation, Tomography, X-Ray Computed, TP248.13-248.65, Mathematics, Algorithms, Biotechnology

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/40083281
https://doaj.org/article/f866edd1304847f0b34485c8470e0bb3
https://orbilu.uni.lu/handle/10993/67098
https://orbilu.uni.lu/bitstream/10993/67098/1/30UncertaintyCNN.pdf

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization: Impact of image reconstruction and harmonization

Συγγραφείς: Gharibi, Omid, Hajianfar, Ghasem, Sabouri, Maziar, Mohebi, Mobin, Bagheri, Soroush, Arian, Fatemeh, Yasemi, Mohammad Javad, Bitarafan Rajabi, Ahmad, Rahmim, Arman, Zaidi, Habib, Shiri Lord, Isaac

Πηγή: Med Phys
Gharibi, O, Hajianfar, G, Sabouri, M, Mohebi, M, Bagheri, S, Arian, F, Yasemi, M J, Bitarafan Rajabi, A, Rahmim, A, Zaidi, H & Shiri, I 2025, 'Myocardial perfusion SPECT radiomic features reproducibility assessment : Impact of image reconstruction and harmonization', Medical Physics, vol. 52, no. 2, pp. 965-977. https://doi.org/10.1002/mp.17490

Θεματικοί όροι: Male, Coronary Artery Disease / diagnostic imaging, Image Processing, Computer-Assisted/methods, Image Processing, MPI-SPECT, Coronary Artery Disease, QUANTITATIVE IMAGING AND IMAGE PROCESSING, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Tomography, reproducibility, Tomography, Emission-Computed, Single-Photon, MPI‐SPECT, Radiomics, 616.0757, Myocardial Perfusion Imaging, Reproducibility of Results, Middle Aged, Reproducibility, Myocardial Perfusion Imaging/methods, Image Processing, Computer-Assisted / methods, Harmonization, radiomics, harmonization, Female, Emission-Computed, Single-Photon, Coronary Artery Disease/diagnostic imaging

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/39470363
https://findresearcher.sdu.dk/ws/files/283199712/Medical_Physics_-_2024_-_Gharibi_-_Myocardial_perfusion_SPECT_radiomic_features_reproducibility_assessment_Impact_of_image.pdf
https://doi.org/10.1002/mp.17490
https://portal.findresearcher.sdu.dk/da/publications/efb6c474-db0b-4468-a76c-f836207755e4

Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy: a case study in wireless capsule endoscopy

Συγγραφείς: Panagiota Gatoula, Dimitrios E. Diamantis, Anastasios Koulaouzidis, Cristina Carretero, Stefania Chetcuti-Zammit, Pablo Cortegoso Valdivia, Begoña González-Suárez, Alessandro Mussetto, John Plevris, Alexander Robertson, Bruno Rosa, Ervin Toth, Dimitris K. Iakovidis

Πηγή: Sci Rep
Gatoula, P, Diamantis, D E, Koulaouzidis, A, Carretero, C, Chetcuti-Zammit, S, Valdivia, P C, González-Suárez, B, Mussetto, A, Plevris, J, Robertson, A, Rosa, B, Toth, E & Iakovidis, D K 2025, 'Clinical evaluation of medical image synthesis : a case study in wireless capsule endoscopy', Scientific Reports, vol. 15, 35068. https://doi.org/10.1038/s41598-025-14359-4
Scientific Reports

Θεματικοί όροι: FOS: Computer and information sciences, Capsule Endoscopy/methods, Image Processing, Computer-Assisted/methods, Computer Science - Artificial Intelligence, Image Processing, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Inflammatory Bowel Diseases, Capsule Endoscopy, Inflammatory Bowel Diseases/diagnostic imaging, Article, Artificial Intelligence (cs.AI), Artificial Intelligence, Computer-Assisted/methods, Image Processing, Computer-Assisted, FOS: Electrical engineering, electronic engineering, information engineering, Humans

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/41062527
https://pmc.ncbi.nlm.nih.gov/articles/PMC12508211/
https://pubmed.ncbi.nlm.nih.gov/41062527/
https://doi.org/10.1038/s41598-025-14359-4
http://arxiv.org/abs/2411.00178
https://portal.findresearcher.sdu.dk/da/publications/676d7644-1480-4207-a9dc-c7acbe8ef8a1
https://doi.org/10.1038/s41598-025-14359-4
https://portal.findresearcher.sdu.dk/da/publications/676d7644-1480-4207-a9dc-c7acbe8ef8a1
https://doi.org/10.1038/s41598-025-14359-4
https://findresearcher.sdu.dk/ws/files/297844105/s41598-025-14359-4.pdf

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18F-FDG PET Scans: Application to 18F-FDG PET Scans

Συγγραφείς: Amini, Mehdi, Salimi, Yazdan, Hajianfar, Ghasem, Mainta, Ismini Charis, Hervier, Elsa, Sanaat, Amirhossein, Rahmim, Arman, Shiri Lord, Isaac, Zaidi, Habib

Πηγή: Amini, M, Salimi, Y, Hajianfar, G, Mainta, I, Hervier, E, Sanaat, A, Rahmim, A, Shiri, I & Zaidi, H 2024, 'Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment : Application to 18 F-FDG PET Scans', Clinical Nuclear Medicine, vol. 49, no. 12, pp. 1079-1090. https://doi.org/10.1097/RLU.0000000000005526

Θεματικοί όροι: Positron-Emission Tomography / methods, Male, Quality Control, Adult, 616.0757, Image Processing, Computer-Assisted/methods, Image Processing, image quality assessment, deep learning, Middle Aged, Positron-Emission Tomography/standards, Automation, PET, Image Processing, Computer-Assisted / methods, image analysis, radiomics, Fluorodeoxyglucose F18, Positron-Emission Tomography, Computer-Assisted/methods, Positron-Emission Tomography / standards, Image Processing, Computer-Assisted, Humans, Female, Whole Body Imaging, Aged

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/39466652
https://research.rug.nl/en/publications/ae458255-d5b8-48c7-bada-7b32f6595ab3
https://hdl.handle.net/11370/ae458255-d5b8-48c7-bada-7b32f6595ab3
https://doi.org/10.1097/RLU.0000000000005526
https://archive-ouverte.unige.ch/unige:181804
https://doi.org/10.1097/rlu.0000000000005526
https://findresearcher.sdu.dk/ws/files/276709183/fully_automated_region_specific.2.pdf
https://doi.org/10.1097/RLU.0000000000005526
https://portal.findresearcher.sdu.dk/da/publications/5c507bd9-2d37-447f-b1cb-0f0f5c214db5

Highly efficient image navigator based 3D whole‐heart cardiac MRA at 0.55T

Συγγραφείς: Carlos Castillo‐Passi, Karl P. Kunze, Michael G. Crabb, Camila Munoz, Anastasia Fotaki, Radhouene Neji, Pablo Irarrazaval, Claudia Prieto, René M. Botnar

Πηγή: Magn Reson Med
Castillo-Passi, C, Kunze, K P, Crabb, M G, Munoz, C, Fotaki, A, Neji, R, Irarrazaval, P, Prieto, C & Botnar, R M 2025, 'Highly efficient image navigator based 3D whole-heart cardiac MRA at 0.55T', Magnetic Resonance in Medicine, vol. 93, no. 2, pp. 689-698. https://doi.org/10.1002/mrm.30316

Θεματικοί όροι: Male, Adult, Image Processing, Computer-Assisted/methods, Coronary Vessels/diagnostic imaging, Signal-To-Noise Ratio, Imaging, Three-Dimensional, 03 Salud y bienestar, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Humans, Whole-heart, Image Interpretation, Computer-Assisted/methods, Low-field MRI, Reproducibility of Results, Heart, Bloch equations, Heart/diagnostic imaging, Image Enhancement, Imaging Methodology, Coronary Vessels, Imaging, Three-Dimensional/methods, Magnetic Resonance Angiography/methods, MRA, 03 Good Health and Well-being, Female, Image Enhancement/methods, Magnetic Resonance Angiography, Algorithms

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/39415543

Deep Learning–Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging

Συγγραφείς: Yazdan Salimi, Zahra Mansouri, Isaac Shiri, Ismini Mainta, Habib Zaidi

Πηγή: Clin Nucl Med
Salimi, Y, Mansouri, Z, Shiri, I, Mainta, I & Zaidi, H 2025, 'Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images : A Solution for Unreliable CT Segmentation in PET/CT Imaging', Clinical Nuclear Medicine, vol. 50, no. 4, pp. 289-300. https://doi.org/10.1097/RLU.0000000000005685

Θεματικοί όροι: Male, Image Processing, Computer-Assisted/methods, PET/CT, Image Processing, organ segmentation, F-FDG, deep learning, Original Articles, Middle Aged, Deep Learning, Ga-PSMA, Fluorodeoxyglucose F18, Positron Emission Tomography Computed Tomography, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Female, Aged

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/39883026
https://portal.findresearcher.sdu.dk/da/publications/355a7aaf-d9b0-46c2-8712-3697ae1db0ab
https://findresearcher.sdu.dk/ws/files/287379807/deep_learning_powered_ct_less_multitracer_organ.2.pdf
https://doi.org/10.1097/RLU.0000000000005685
https://portal.findresearcher.sdu.dk/da/publications/355a7aaf-d9b0-46c2-8712-3697ae1db0ab
https://doi.org/10.1097/RLU.0000000000005685

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study: A simulation study

Συγγραφείς: Xin, Lin, Zhuo, Weihai, Liu, Qian, Xie, Tianwu, Zaidi, Habib

Πηγή: Z Med Phys
Zeitschrift für Medizinische Physik, Vol 34, Iss 3, Pp 408-418 (2024)
Xin, L, Zhuo, W, Liu, Q, Xie, T & Zaidi, H 2024, 'Triple-source saddle-curve cone-beam photon counting CT image reconstruction : A simulation study', Zeitschrift fuer Medizinische Physik, vol. 34, no. 3, pp. 408-418. https://doi.org/10.1016/j.zemedi.2022.10.003

Θεματικοί όροι: Simulation study, 616.8, Image Processing, Computer-Assisted/methods, Saddle curve CT, Image Processing, R895-920, Phantoms, Imaging, Medical physics. Medical radiology. Nuclear medicine, 03 medical and health sciences, 0302 clinical medicine, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Computer Simulation, Cone-Beam Computed Tomography / methods, Original Paper, Photons, 616.0757, Triple-source, Phantoms, Imaging, Cone-Beam Computed Tomography, Cone-Beam Computed Tomography/methods, Image Processing, Computer-Assisted / methods, Photon counting CT, Exact reconstruction, Algorithms

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/36336554
https://doaj.org/article/354f2c61071245dd820b12f11480ffca
https://hdl.handle.net/11370/b99e0eb0-df27-47c4-a573-d11ed47c1f5f
https://research.rug.nl/en/publications/b99e0eb0-df27-47c4-a573-d11ed47c1f5f
https://doi.org/10.1016/j.zemedi.2022.10.003
https://archive-ouverte.unige.ch/unige:167022
https://doi.org/10.1016/j.zemedi.2022.10.003
https://findresearcher.sdu.dk/ws/files/269728117/1-s2.0-S0939388922000976-main.pdf
https://doi.org/10.1016/j.zemedi.2022.10.003
https://portal.findresearcher.sdu.dk/da/publications/2dbafa9a-c744-4e9d-b5e9-9817716cfd8f

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images

Συγγραφείς: Arabi, Hossein, Zaidi, Habib

Πηγή: J Imaging Inform Med
Arabi, H & Zaidi, H 2024, 'Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images', Journal of Imaging Informatics in Medicine, vol. 37, no. 6, pp. 3217-3230. https://doi.org/10.1007/s10278-024-01159-x

Θεματικοί όροι: Data augmentation, Image Processing, Computer-Assisted/methods, Image Processing, Tomography, X-Ray Computed/methods, Kidney, Hippocampus, Article, Magnetic Resonance Imaging/methods, 03 medical and health sciences, Segmentation, Deep Learning, 0302 clinical medicine, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Kidney/diagnostic imaging, Self-learning, Tomography, 616.0757, Brain Neoplasms, Deep learning, Contrastive-learning, Magnetic Resonance Imaging, X-Ray Computed/methods, Semantics, Brain Neoplasms/diagnostic imaging, Tomography, X-Ray Computed, Hippocampus/diagnostic imaging

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/38858260
https://research.rug.nl/en/publications/a45dd6b1-47f3-46ef-8801-bc5800c963d6
https://doi.org/10.1007/s10278-024-01159-x
https://hdl.handle.net/11370/a45dd6b1-47f3-46ef-8801-bc5800c963d6
https://portal.findresearcher.sdu.dk/da/publications/bfb80140-b53d-4520-8868-37daa2da556b
https://doi.org/10.1007/s10278-024-01159-x
https://findresearcher.sdu.dk/ws/files/279085122/s10278-024-01159-x.pdf
https://portal.findresearcher.sdu.dk/da/publications/bfb80140-b53d-4520-8868-37daa2da556b
https://doi.org/10.1007/s10278-024-01159-x

A survey on brain tumor image analysis

Συγγραφείς: Sailunaz, Kashfia, Alhajj, Sleiman, Özyer, Tansel, Rokne, Jon, Alhajj, Reda

Πηγή: Sailunaz, K, Alhajj, S, Özyer, T, Rokne, J & Alhajj, R 2024, 'A survey on brain tumor image analysis', Medical & Biological Engineering & Computing, vol. 62, no. 1, pp. 1-45. https://doi.org/10.1007/s11517-023-02873-4

Θεματικοί όροι: Tumor detection, Tumor features, Image Processing, Computer-Assisted/methods, Brain Neoplasms, Image Processing, Tumor segmentation, Brain, Deep learning, Brain/diagnostic imaging, Magnetic Resonance Imaging, 3. Good health, Brain tumor, Machine Learning, Medical image analysis, Machine learning, Brain Neoplasms/diagnostic imaging, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, MRI

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/37700082
https://portal.findresearcher.sdu.dk/da/publications/2c3456e6-9f95-4388-867e-f418c1f5c753
https://doi.org/10.1007/s11517-023-02873-4

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy

Συγγραφείς: Mansouri, Zahra, Salimi, Yazdan, Akhavanallaf, Azadeh, Shiri Lord, Isaac, Andrade Teixeira, Eliluane Perazio, Hou, Xinchi, Beauregard, Jean-Mathieu, Rahmim, Arman, Zaidi, Habib

Πηγή: Eur J Nucl Med Mol Imaging
Mansouri, Z, Salimi, Y, Akhavanallaf, A, Shiri, I, Teixeira, E P A, Hou, X, Beauregard, J M, Rahmim, A & Zaidi, H 2024, 'Deep transformer-based personalized dosimetry from SPECT/CT images : a hybrid approach for [ 177 Lu]Lu-DOTATATE radiopharmaceutical therapy', European Journal of Nuclear Medicine and Molecular Imaging, vol. 51, no. 6, pp. 1516-1529. https://doi.org/10.1007/s00259-024-06618-9
European Journal of Nuclear Medicine and Molecular Imaging

Θεματικοί όροι: Male, Single Photon Emission Computed Tomography Computed Tomography / methods, Single Photon Emission Computed Tomography Computed Tomography, Image Processing, Computer-Assisted/methods, Image Processing, [Lu]Lu-DOTATATE, [177Lu]Lu-DOTATATE, Radionuclide therapy, Octreotide, Radiopharmaceuticals/therapeutic use, Neuroendocrine Tumors/radiotherapy, Neuroendocrine Tumors / radiotherapy, Deep Learning, Radiometry / methods, Radiation dosimetry, Computer-Assisted/methods, Radiopharmaceuticals / therapeutic use, Organometallic Compounds, Image Processing, Computer-Assisted, Humans, Octreotide/analogs & derivatives, Radiometry/methods, Precision Medicine, Radiometry, Monte Carlo simulation, 616.0757, Octreotide / therapeutic use, Deep learning, Precision Medicine/methods, Neuroendocrine Tumors / diagnostic imaging, 3. Good health, Neuroendocrine Tumors, Single Photon Emission Computed Tomography Computed Tomography/methods, Image Processing, Computer-Assisted / methods, Organometallic Compounds / therapeutic use, Octreotide / analogs & derivatives, Organometallic Compounds/therapeutic use, Original Article, Female, Precision Medicine / methods, Radiopharmaceuticals, Monte Carlo Method

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/38267686
https://research.rug.nl/en/publications/0c5bb404-8ce2-43d0-98b5-265d7435d8f6
https://hdl.handle.net/11370/0c5bb404-8ce2-43d0-98b5-265d7435d8f6
https://doi.org/10.1007/s00259-024-06618-9
https://archive-ouverte.unige.ch/unige:176796
https://doi.org/10.1007/s00259-024-06618-9
https://portal.findresearcher.sdu.dk/da/publications/5586451c-fbc7-471d-96b4-81628f1a26b2
https://doi.org/10.1007/s00259-024-06618-9
https://portal.findresearcher.sdu.dk/da/publications/5586451c-fbc7-471d-96b4-81628f1a26b2
https://findresearcher.sdu.dk/ws/files/260122448/s00259-024-06618-9.pdf
https://doi.org/10.1007/s00259-024-06618-9

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement

Συγγραφείς: Shiri Lord, Isaac, Salimi, Yazdan, Maghsudi, Mehdi, Jenabi, Elnaz, Harsini, Sara, Razeghi, Behrooz, Mostafaei, Shayan, Hajianfar, Ghasem, Sanaat, Amirhossein, Jafari, Esmail, Samimi, Rezvan, Khateri, Maziar, Sheikhzadeh, Peyman, Geramifar, Parham, Dadgar, Habibollah, Bitrafan Rajabi, Ahmad, Assadi, Majid, Bénard, François, Vafaei Sadr, Alireza, Voloshynovskyy, Svyatoslav, Mainta, Ismini Charis, Uribe, Carlos, Rahmim, Arman, Zaidi, Habib

Πηγή: Eur J Nucl Med Mol Imaging
Shiri, I, Salimi, Y, Maghsudi, M, Jenabi, E, Harsini, S, Razeghi, B, Mostafaei, S, Hajianfar, G, Sanaat, A, Jafari, E, Samimi, R, Khateri, M, Sheikhzadeh, P, Geramifar, P, Dadgar, H, Bitrafan Rajabi, A, Assadi, M, Bénard, F, Vafaei Sadr, A, Voloshynovskiy, S, Mainta, I, Uribe, C, Rahmim, A & Zaidi, H 2023, 'Differential privacy preserved federated transfer learning for multi-institutional 68 Ga-PET image artefact detection and disentanglement', European Journal of Nuclear Medicine and Molecular Imaging, vol. 51, no. 1, pp. 40-53. https://doi.org/10.1007/s00259-023-06418-7

Θεματικοί όροι: Male, Positron Emission Tomography Computed Tomography / methods, Image Processing, Computer-Assisted/methods, PET/CT, Image Processing, Federated learning, Gallium Radioisotopes, Artefacts, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Positron Emission Tomography Computed Tomography, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, 025.063, Positron-Emission Tomography / methods, 616.0757, Prostatic Neoplasms, Deep learning, Positron-Emission Tomography/methods, 3. Good health, Image Processing, Computer-Assisted / methods, Privacy, Positron-Emission Tomography, Original Article, Artifacts, Positron Emission Tomography Computed Tomography/methods

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/37682303

Information fusion for fully automated segmentation of head and neck tumors from PET and CT images

Συγγραφείς: Shiri Lord, Isaac, Amini, Mehdi, Yousefirizi, Fereshteh, Vafaei Sadr, Alireza, Hajianfar, Ghasem, Salimi, Yazdan, Mansouri, Zahra, Jenabi, Elnaz, Maghsudi, Mehdi, Mainta, Ismini Charis, Becker, Minerva, Rahmim, Arman, Zaidi, Habib

Πηγή: Shiri, I, Amini, M, Yousefirizi, F, Vafaei Sadr, A, Hajianfar, G, Salimi, Y, Mansouri, Z, Jenabi, E, Maghsudi, M, Mainta, I, Becker, M, Rahmim, A & Zaidi, H 2024, 'Information fusion for fully automated segmentation of head and neck tumors from PET and CT images', Medical Physics, vol. 51, no. 1, pp. 319-333. https://doi.org/10.1002/mp.16615

Θεματικοί όροι: fusion, 616.8, 616.0757, Image Processing, Computer-Assisted/methods, PET/CT, Image Processing, segmentation, deep learning, Deep learning, 3. Good health, Segmentation, Head and Neck Neoplasms, Head and Neck Neoplasms/diagnostic imaging, Positron Emission Tomography Computed Tomography, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, head and neck cancer, Fusion, Head and neck cancer, Positron Emission Tomography Computed Tomography/methods, Algorithms

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/37475591
https://hdl.handle.net/11370/37a28dd8-d703-48fe-a01d-faf9383815cf
https://research.rug.nl/en/publications/37a28dd8-d703-48fe-a01d-faf9383815cf
https://doi.org/10.1002/mp.16615
https://archive-ouverte.unige.ch/unige:170500
https://doi.org/10.1002/mp.16615
https://findresearcher.sdu.dk/ws/files/252854539/Medical_Physics_-_2023_-_Shiri_-_Information_fusion_for_fully_automated_segmentation_of_head_and_neck_tumors_from_PET_and.pdf
https://portal.findresearcher.sdu.dk/da/publications/50c0ebc9-132e-43e9-aaa7-0bb23f3f160b
https://doi.org/10.1002/mp.16615
https://portal.findresearcher.sdu.dk/da/publications/50c0ebc9-132e-43e9-aaa7-0bb23f3f160b
https://doi.org/10.1002/mp.16615

Enhancing fetal ultrasound image quality and anatomical plane recognition in low-resource settings using super-resolution models

Συγγραφείς: Boumeridja, Hafida, Ammar, Mohammed, Alzubaidi, Mahmood, Mahmoudi, Saïd, Benamer, Lamya Nawal, Agus, Marco, Househ, Mowafa, Lekadir, Karim, El Habib Daho, Mostafa

Συνεισφορές: Hamad Bin Khalifa University, Qatar

Πηγή: Sci Rep
Scientific Reports, Vol 15, Iss 1, Pp 1-26 (2025)

Θεματικοί όροι: Sciences informatiques, Low-resource Settings, Multidisciplinary, Image Processing, Computer-Assisted/methods, Science, Fetal ultrasound image, Computer science, Article, Ultrasonography, Prenatal, Engineering, computing & technology, Ingénierie, informatique & technologie, Fetus, Fetus/diagnostic imaging, Pregnancy, Super-resolution, Image Processing, Computer-Assisted, Medicine, Humans, Female, Ultrasonography, Prenatal/methods, Algorithms

Σύνδεσμος πρόσβασης: https://pubmed.ncbi.nlm.nih.gov/40069254
https://doaj.org/article/45d7434bf52643e586ca754862188b81