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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

Glioblastoma Detection with Hyperspectral Image Analysis through Optimal Wavelength Selection

Authors: Verbers, Max, Manni, Francesca, Fabelo, Himar, Leon, Raquel, Burström, Gustav, Lagares, Alfonso, Piñeiro, Juan F., Morera Molina, Jesus, Marrero Callicó, Gustavo, Zinger, Svitlana

Source: 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). :1-7

Subject Terms: Hyperspectral Imaging/methods, Brain Neoplasms, Image Processing, Brain Neoplasms/diagnostic imaging, Glioblastoma/diagnostic imaging, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Hyperspectral Imaging, Glioblastoma, Algorithms

Access URL: https://pubmed.ncbi.nlm.nih.gov/41337398
https://research.tue.nl/en/publications/ddbd2d9c-99a9-4861-84b2-1182e99010f8
https://doi.org/10.1109/EMBC58623.2025.11252746

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

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

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

Subject Terms: 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

Access URL: 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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

Contributors: Kober, Frank

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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

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

Subject Terms: 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

Access URL: 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

Numerical Simulation of Intravascular Ultrasound Images Based on Patient-Specific Computed Tomography

Authors: Daniek A. C. van Aarle, Floor Fasen, Harold A. W. Schmeitz, Frederik J. de Bruijn, Marc R. H. M. van Sambeek, Hans-Martin Schwab, Richard G. P. Lopata

Source: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 72:215-225

Subject Terms: Image Processing, Abdominal/diagnostic imaging, Phantoms, intravascular ultrasound, Imaging, abdominal aortic aneurysm, virtual patient, Interventional/methods, k-Wave, in silico phantom, ultrasound simulation, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Computer Simulation, cardiovascular imaging, Tomography, Ultrasonography, Interventional, Ultrasonography, intravascular ultrasound (IVUS), Phantoms, Imaging, X-Ray Computed/methods, Aortic Aneurysm, Abdominal aortic aneurysm (AAA), k-wave, Tomography, X-Ray Computed, Algorithms, Aortic Aneurysm, Abdominal

Access URL: https://pubmed.ncbi.nlm.nih.gov/40030621
https://research.tue.nl/en/publications/6b434ea2-360e-4d45-b567-fc345be61808
https://doi.org/10.1109/TUFFC.2024.3523037

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

Authors: 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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

Authors: 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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: https://pubmed.ncbi.nlm.nih.gov/39415543

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images

Authors: Uher, Daniel, Drenthen, Gerhard S., Poser, Benedikt A., Hofman, Paul A.M., Wagner, Louis G., van Lanen, Rick H.G.J., Hoeberigs, Christianne M., Colon, Albert J., Schijns, Olaf E.M.G., Jansen, Jacobus F.A., Backes, Walter H.

Source: Magnetic Resonance Imaging. 110:57-68

Subject Terms: Adult, Male, Ultra-high field MRI, Artifact removal, Neural Networks, White Matter/diagnostic imaging, Image Processing, Signal-To-Noise Ratio, Magnetic Resonance Imaging, White Matter, Temporal Lobe/diagnostic imaging, Temporal Lobe, Magnetic Resonance Imaging/methods, 3. Good health, Computer, Young Adult, Machine learning, FLAIR, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Female, Neural Networks, Computer, Artifacts

Access URL: https://pubmed.ncbi.nlm.nih.gov/38621552
https://research.tue.nl/en/publications/6a9d59e6-7782-4cde-9c75-16e25a741211
https://doi.org/10.1016/j.mri.2024.04.013

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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

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

Source: 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

Subject Terms: 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

File Description: application/pdf

Access URL: 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

Authors: Arabi, Hossein, Zaidi, Habib

Source: 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

Subject Terms: 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

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Access URL: 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

Ticept: Wideband Electrical Properties Tomography by Tissue Composition Assessment With Quantitative 1H23Na39K Multinuclear MRI

Authors: Barendsz, Laura J.C., Sumser, Kemal, Gerhalter, Teresa, Mestrom, Rob M.C., Nagel, Armin, Paulides, M.M. (Maarten)

Source: Magnetic Resonance in Medicine. 95(3):1503-1512

Subject Terms: Male, Adult, Sodium/chemistry, electrical conductivity, Image Processing, potassium MRI, sodium MRI, permittivity, Healthy Volunteers, Phantoms, Magnetic Resonance Imaging/methods, Imaging, electrical properties tomography (EPT), mixture theory, Three-Dimensional, Electric Impedance, Computer-Assisted/methods, Humans, tissue water content, Computer Simulation, Female, Potassium/chemistry, Algorithms, Tomography/methods

Access URL: https://pure.eur.nl/en/publications/39d68449-7aa8-4197-bb4d-3e3ae26b1721
https://doi.org/10.1002/mrm.70139
https://research.tue.nl/en/publications/8ebb15ad-abee-4a3f-926e-60f300b90a07
https://doi.org/10.1002/mrm.70139

Estimating Surgical Urethral Length on Intraoperative Robot-Assisted Prostatectomy Images Using Artificial Intelligence Anatomy Recognition

Authors: Aron F.H.A. Bakker, Joris V. de Nijs, Tim J.M. Jaspers, Peter H.N. de With, Alexander J.W. Beulens, Henk G. van der Poel, Fons van der Sommen, Willem M. Brinkman

Source: Journal of Endourology. 38:690-696

Subject Terms: Male, Neural Networks, Urethra/surgery, Image Processing, SDG 3 – Goede gezondheid en welzijn, Computer, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Urethra, Robotic Surgical Procedures, Artificial Intelligence, Computer-Assisted/methods, Image Processing, Computer-Assisted, Humans, Prostatectomy, Prostate/surgery, continence, Robotic Surgical Procedures/methods, Prostate, artificial intelligence, prostate cancer, anatomy recognition, 3. Good health, Prostatectomy/methods, urethral length, Neural Networks, Computer

Access URL: https://pubmed.ncbi.nlm.nih.gov/38613819