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Showing 85 to 94 of 94 entries
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A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images.

Sensors (Basel, Switzerland)

Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N.
PMID: 33810066
Sensors (Basel). 2021 Mar 22;21(6). doi: 10.3390/s21062215.

Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in...

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Voulodimos A, Protopapadakis E, Katsamenis I, et al. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors (Basel). 2021;21(6)doi: 10.3390/s21062215.
Voulodimos, A., Protopapadakis, E., Katsamenis, I., Doulamis, A., & Doulamis, N. (2021). A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors (Basel, Switzerland), 21(6), . https://doi.org/10.3390/s21062215
Voulodimos, Athanasios, et al. "A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images." Sensors (Basel, Switzerland) vol. 21,6 (2021). doi: https://doi.org/10.3390/s21062215
Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors (Basel). 2021 Mar 22;21(6). doi: 10.3390/s21062215. PMID: 33810066; PMCID: PMC8004971.
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Contribution of CT Features in the Diagnosis of COVID-19.

Canadian respiratory journal

Zuo H.
PMID: 33224361
Can Respir J. 2020 Nov 12;2020:1237418. doi: 10.1155/2020/1237418. eCollection 2020.

The outbreak of novel coronavirus disease 2019 (COVID-19) first occurred in Wuhan, Hubei Province, China, and spread across the country and worldwide quickly. It has been defined as a major global health emergency by the World Health Organization (WHO)....

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Zuo H. Contribution of CT Features in the Diagnosis of COVID-19. Can Respir J. 2020;2020:1237418doi: 10.1155/2020/1237418.
Zuo, H. (2020). Contribution of CT Features in the Diagnosis of COVID-19. Canadian respiratory journal, 20201237418. https://doi.org/10.1155/2020/1237418
Zuo, Houdong. "Contribution of CT Features in the Diagnosis of COVID-19." Canadian respiratory journal vol. 2020 (2020): 1237418. doi: https://doi.org/10.1155/2020/1237418
Zuo H. Contribution of CT Features in the Diagnosis of COVID-19. Can Respir J. 2020 Nov 12;2020:1237418. doi: 10.1155/2020/1237418. eCollection 2020. PMID: 33224361; PMCID: PMC7670585.
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A linear density on imaging: Non-contrast CT as a useful localisation method.

Annals of the Academy of Medicine, Singapore

Blankenstein TN, Smith DAJ, Cheng AJL.
PMID: 34472566
Ann Acad Med Singap. 2021 Aug;50(8):660-661.

No abstract available.

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Blankenstein TN, Smith DAJ, Cheng AJL. A linear density on imaging: Non-contrast CT as a useful localisation method. Ann Acad Med Singap. 2021;50(8):660-661
Blankenstein, T. N., Smith, D. A. J., & Cheng, A. J. L. (2021). A linear density on imaging: Non-contrast CT as a useful localisation method. Annals of the Academy of Medicine, Singapore, 50(8), 660-661.
Blankenstein, Tom N, et al. "A linear density on imaging: Non-contrast CT as a useful localisation method." Annals of the Academy of Medicine, Singapore vol. 50,8 (2021): 660-661.
Blankenstein TN, Smith DAJ, Cheng AJL. A linear density on imaging: Non-contrast CT as a useful localisation method. Ann Acad Med Singap. 2021 Aug;50(8):660-661. PMID: 34472566.
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Critical Re-Evaluation of a Failure Mode Effect Analysis in a Radiation Therapy Department After 10 Years.

Practical radiation oncology

Mancosu P, Signori C, Clerici E, Comito T, D'Agostino GR, Franceschini D, Franzese C, Lobefalo F, Navarria P, Paganini L, Reggiori G, Tomatis S, Scorsetti M.
PMID: 33197646
Pract Radiat Oncol. 2021 May-Jun;11(3):e329-e338. doi: 10.1016/j.prro.2020.11.002. Epub 2020 Nov 13.

PURPOSE: Failure mode effect analysis (FMEA) is a proactive methodology that allows one to analyze a process, regardless of whether an adverse event occurs. In our radiation therapy (RT) department, a first FMEA was performed in 2009. In this...

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Mancosu P, Signori C, Clerici E, et al. Critical Re-Evaluation of a Failure Mode Effect Analysis in a Radiation Therapy Department After 10 Years. Pract Radiat Oncol. 2020;11(3):e329-e338doi: 10.1016/j.prro.2020.11.002.
Mancosu, P., Signori, C., Clerici, E., Comito, T., D'Agostino, G. R., Franceschini, D., Franzese, C., Lobefalo, F., Navarria, P., Paganini, L., Reggiori, G., Tomatis, S., & Scorsetti, M. (2021). Critical Re-Evaluation of a Failure Mode Effect Analysis in a Radiation Therapy Department After 10 Years. Practical radiation oncology, 11(3), e329-e338. https://doi.org/10.1016/j.prro.2020.11.002
Mancosu, Pietro, et al. "Critical Re-Evaluation of a Failure Mode Effect Analysis in a Radiation Therapy Department After 10 Years." Practical radiation oncology vol. 11,3 (2021): e329-e338. doi: https://doi.org/10.1016/j.prro.2020.11.002
Mancosu P, Signori C, Clerici E, Comito T, D'Agostino GR, Franceschini D, Franzese C, Lobefalo F, Navarria P, Paganini L, Reggiori G, Tomatis S, Scorsetti M. Critical Re-Evaluation of a Failure Mode Effect Analysis in a Radiation Therapy Department After 10 Years. Pract Radiat Oncol. 2021 May-Jun;11(3):e329-e338. doi: 10.1016/j.prro.2020.11.002. Epub 2020 Nov 13. PMID: 33197646.
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Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning.

Journal of applied clinical medical physics

Zheng Y, Jiang S, Yang Z.
PMID: 34505341
J Appl Clin Med Phys. 2021 Oct;22(10):22-35. doi: 10.1002/acm2.13392. Epub 2021 Sep 10.

PURPOSE: The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image registration. However, the nonlinear deformation and large-scale displacement of lung tissues caused by respiratory motion...

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Zheng Y, Jiang S, Yang Z. Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning. J Appl Clin Med Phys. 2021;22(10):22-35doi: 10.1002/acm2.13392.
Zheng, Y., Jiang, S., & Yang, Z. (2021). Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning. Journal of applied clinical medical physics, 22(10), 22-35. https://doi.org/10.1002/acm2.13392
Zheng, Yongnan, et al. "Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning." Journal of applied clinical medical physics vol. 22,10 (2021): 22-35. doi: https://doi.org/10.1002/acm2.13392
Zheng Y, Jiang S, Yang Z. Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning. J Appl Clin Med Phys. 2021 Oct;22(10):22-35. doi: 10.1002/acm2.13392. Epub 2021 Sep 10. PMID: 34505341; PMCID: PMC8504612.
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Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Abramova V, Clèrigues A, Quiles A, Figueredo DG, Silva Y, Pedraza S, Oliver A, Lladó X.
PMID: 33901919
Comput Med Imaging Graph. 2021 Jun;90:101908. doi: 10.1016/j.compmedimag.2021.101908. Epub 2021 Apr 14.

Hemorrhagic stroke is the condition involving the rupture of a vessel inside the brain and is characterized by high mortality rates. Even if the patient survives, stroke can cause temporary or permanent disability depending on how long blood flow...

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Abramova V, Clèrigues A, Quiles A, et al. Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks. Comput Med Imaging Graph. 2021;90:101908doi: 10.1016/j.compmedimag.2021.101908.
Abramova, V., Clèrigues, A., Quiles, A., Figueredo, D. G., Silva, Y., Pedraza, S., Oliver, A., & Lladó, X. (2021). Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 90101908. https://doi.org/10.1016/j.compmedimag.2021.101908
Abramova, Valeriia, et al. "Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks." Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 90 (2021): 101908. doi: https://doi.org/10.1016/j.compmedimag.2021.101908
Abramova V, Clèrigues A, Quiles A, Figueredo DG, Silva Y, Pedraza S, Oliver A, Lladó X. Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks. Comput Med Imaging Graph. 2021 Jun;90:101908. doi: 10.1016/j.compmedimag.2021.101908. Epub 2021 Apr 14. PMID: 33901919.
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Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Trajanovski S, Mavroeidis D, Swisher CL, Gebre BG, Veeling BS, Wiemker R, Klinder T, Tahmasebi A, Regis SM, Wald C, McKee BJ, Flacke S, MacMahon H, Pien H.
PMID: 33895622
Comput Med Imaging Graph. 2021 Jun;90:101883. doi: 10.1016/j.compmedimag.2021.101883. Epub 2021 Mar 05.

PURPOSE: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the...

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Trajanovski S, Mavroeidis D, Swisher CL, et al. Towards radiologist-level cancer risk assessment in CT lung screening using deep learning. Comput Med Imaging Graph. 2021;90:101883doi: 10.1016/j.compmedimag.2021.101883.
Trajanovski, S., Mavroeidis, D., Swisher, C. L., Gebre, B. G., Veeling, B. S., Wiemker, R., Klinder, T., Tahmasebi, A., Regis, S. M., Wald, C., McKee, B. J., Flacke, S., MacMahon, H., & Pien, H. (2021). Towards radiologist-level cancer risk assessment in CT lung screening using deep learning. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 90101883. https://doi.org/10.1016/j.compmedimag.2021.101883
Trajanovski, Stojan, et al. "Towards radiologist-level cancer risk assessment in CT lung screening using deep learning." Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 90 (2021): 101883. doi: https://doi.org/10.1016/j.compmedimag.2021.101883
Trajanovski S, Mavroeidis D, Swisher CL, Gebre BG, Veeling BS, Wiemker R, Klinder T, Tahmasebi A, Regis SM, Wald C, McKee BJ, Flacke S, MacMahon H, Pien H. Towards radiologist-level cancer risk assessment in CT lung screening using deep learning. Comput Med Imaging Graph. 2021 Jun;90:101883. doi: 10.1016/j.compmedimag.2021.101883. Epub 2021 Mar 05. PMID: 33895622.
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Reply to: non-contrast CT KUB still has a central role in the management of patients suspected of nephrolithiasis.

Emergency medicine journal : EMJ

Shyy W, Knight R.
PMID: 33355312
Emerg Med J. 2021 Feb;38(2):165. doi: 10.1136/emermed-2020-210728. Epub 2020 Dec 21.

No abstract available.

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Shyy W, Knight R. Reply to: non-contrast CT KUB still has a central role in the management of patients suspected of nephrolithiasis. Emerg Med J. 2020;38(2):165doi: 10.1136/emermed-2020-210728.
Shyy, W., & Knight, R. (2021). Reply to: non-contrast CT KUB still has a central role in the management of patients suspected of nephrolithiasis. Emergency medicine journal : EMJ, 38(2), 165. https://doi.org/10.1136/emermed-2020-210728
Shyy, William, and Knight, Roneesha. "Reply to: non-contrast CT KUB still has a central role in the management of patients suspected of nephrolithiasis." Emergency medicine journal : EMJ vol. 38,2 (2021): 165. doi: https://doi.org/10.1136/emermed-2020-210728
Shyy W, Knight R. Reply to: non-contrast CT KUB still has a central role in the management of patients suspected of nephrolithiasis. Emerg Med J. 2021 Feb;38(2):165. doi: 10.1136/emermed-2020-210728. Epub 2020 Dec 21. PMID: 33355312.
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Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Sensors (Basel, Switzerland)

Souza LFF, Silva ICL, Marques AG, Silva FHDS, Nunes VX, Hassan MM, Albuquerque VHC, Filho PPR.
PMID: 33255308
Sensors (Basel). 2020 Nov 24;20(23). doi: 10.3390/s20236711.

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million...

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Souza LFF, Silva ICL, Marques AG, et al. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel). 2020;20(23)doi: 10.3390/s20236711.
Souza, L. F. F., Silva, I. C. L., Marques, A. G., Silva, F. H. D. S., Nunes, V. X., Hassan, M. M., Albuquerque, V. H. C., & Filho, P. P. R. (2020). Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel, Switzerland), 20(23), . https://doi.org/10.3390/s20236711
Souza, Luís Fabrício de Freitas, et al. "Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation." Sensors (Basel, Switzerland) vol. 20,23 (2020). doi: https://doi.org/10.3390/s20236711
Souza LFF, Silva ICL, Marques AG, Silva FHDS, Nunes VX, Hassan MM, Albuquerque VHC, Filho PPR. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel). 2020 Nov 24;20(23). doi: 10.3390/s20236711. PMID: 33255308; PMCID: PMC7727680.
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Consider a CT angiogram before invasive coronary angiogram in patients with NSTEMI.

BMJ evidence-based medicine

O'Sullivan J.
PMID: 33203622
BMJ Evid Based Med. 2021 Dec;26(6):e12. doi: 10.1136/bmjebm-2020-111402. Epub 2020 Nov 17.

No abstract available.

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O'Sullivan J. Consider a CT angiogram before invasive coronary angiogram in patients with NSTEMI. BMJ Evid Based Med. 2020;26(6):e12doi: 10.1136/bmjebm-2020-111402.
O'Sullivan, J. (2021). Consider a CT angiogram before invasive coronary angiogram in patients with NSTEMI. BMJ evidence-based medicine, 26(6), e12. https://doi.org/10.1136/bmjebm-2020-111402
O'Sullivan, Jack. "Consider a CT angiogram before invasive coronary angiogram in patients with NSTEMI." BMJ evidence-based medicine vol. 26,6 (2021): e12. doi: https://doi.org/10.1136/bmjebm-2020-111402
O'Sullivan J. Consider a CT angiogram before invasive coronary angiogram in patients with NSTEMI. BMJ Evid Based Med. 2021 Dec;26(6):e12. doi: 10.1136/bmjebm-2020-111402. Epub 2020 Nov 17. PMID: 33203622.
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Showing 85 to 94 of 94 entries