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Minute changes of cerebral parenchymal non-contrast computed tomography attenuation reflect cerebral volume alterations of blood.

The neuroradiology journal

Uchizawa T.
PMID: 33657930
Neuroradiol J. 2021 Aug;34(4):341-347. doi: 10.1177/1971400921998981. Epub 2021 Mar 03.

AIMS: Non-infarcted acute cerebral ischaemic areas appear hypo-attenuated on non-contrast narrow-window computed tomography images. We aimed to determine the mechanism underlying minute computed tomography hypo-attenuation and visualise these attenuation changes on non-contrast computed tomography images.METHODS: The cerebral parenchyma was...

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Uchizawa T. Minute changes of cerebral parenchymal non-contrast computed tomography attenuation reflect cerebral volume alterations of blood. Neuroradiol J. 2021;34(4):341-347doi: 10.1177/1971400921998981.
Uchizawa, T. (2021). Minute changes of cerebral parenchymal non-contrast computed tomography attenuation reflect cerebral volume alterations of blood. The neuroradiology journal, 34(4), 341-347. https://doi.org/10.1177/1971400921998981
Uchizawa, Takamitsu. "Minute changes of cerebral parenchymal non-contrast computed tomography attenuation reflect cerebral volume alterations of blood." The neuroradiology journal vol. 34,4 (2021): 341-347. doi: https://doi.org/10.1177/1971400921998981
Uchizawa T. Minute changes of cerebral parenchymal non-contrast computed tomography attenuation reflect cerebral volume alterations of blood. Neuroradiol J. 2021 Aug;34(4):341-347. doi: 10.1177/1971400921998981. Epub 2021 Mar 03. PMID: 33657930; PMCID: PMC8447817.
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LGAN: Lung segmentation in CT scans using generative adversarial network.

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

Tan J, Jing L, Huo Y, Li L, Akin O, Tian Y.
PMID: 33278767
Comput Med Imaging Graph. 2021 Jan;87:101817. doi: 10.1016/j.compmedimag.2020.101817. Epub 2020 Nov 16.

Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing...

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Tan J, Jing L, Huo Y, et al. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput Med Imaging Graph. 2020;87:101817doi: 10.1016/j.compmedimag.2020.101817.
Tan, J., Jing, L., Huo, Y., Li, L., Akin, O., & Tian, Y. (2021). LGAN: Lung segmentation in CT scans using generative adversarial network. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 87101817. https://doi.org/10.1016/j.compmedimag.2020.101817
Tan, Jiaxing, et al. "LGAN: Lung segmentation in CT scans using generative adversarial network." Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 87 (2021): 101817. doi: https://doi.org/10.1016/j.compmedimag.2020.101817
Tan J, Jing L, Huo Y, Li L, Akin O, Tian Y. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput Med Imaging Graph. 2021 Jan;87:101817. doi: 10.1016/j.compmedimag.2020.101817. Epub 2020 Nov 16. PMID: 33278767; PMCID: PMC8477299.
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Generative Deep Learning in Digital Pathology Workflows.

The American journal of pathology

Morrison D, Harris-Birtill D, Caie PD.
PMID: 33838127
Am J Pathol. 2021 Oct;191(10):1717-1723. doi: 10.1016/j.ajpath.2021.02.024. Epub 2021 Apr 08.

Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based...

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Morrison D, Harris-Birtill D, Caie PD. Generative Deep Learning in Digital Pathology Workflows. Am J Pathol. 2021;191(10):1717-1723doi: 10.1016/j.ajpath.2021.02.024.
Morrison, D., Harris-Birtill, D., & Caie, P. D. (2021). Generative Deep Learning in Digital Pathology Workflows. The American journal of pathology, 191(10), 1717-1723. https://doi.org/10.1016/j.ajpath.2021.02.024
Morrison, David, et al. "Generative Deep Learning in Digital Pathology Workflows." The American journal of pathology vol. 191,10 (2021): 1717-1723. doi: https://doi.org/10.1016/j.ajpath.2021.02.024
Morrison D, Harris-Birtill D, Caie PD. Generative Deep Learning in Digital Pathology Workflows. Am J Pathol. 2021 Oct;191(10):1717-1723. doi: 10.1016/j.ajpath.2021.02.024. Epub 2021 Apr 08. PMID: 33838127.
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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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Reply to Kissane and Eggington.

American journal of physiology. Cell physiology

Tropp N, Gilda JE, Cohen S.
PMID: 34874767
Am J Physiol Cell Physiol. 2021 Dec 01;321(6):C1084-C1085. doi: 10.1152/ajpcell.00393.2021.

No abstract available.

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Tropp N, Gilda JE, Cohen S. Reply to Kissane and Eggington. Am J Physiol Cell Physiol. 2021;321(6):C1084-C1085doi: 10.1152/ajpcell.00393.2021.
Tropp, N., Gilda, J. E., & Cohen, S. (2021). Reply to Kissane and Eggington. American journal of physiology. Cell physiology, 321(6), C1084-C1085. https://doi.org/10.1152/ajpcell.00393.2021
Tropp, Nadav, et al. "Reply to Kissane and Eggington." American journal of physiology. Cell physiology vol. 321,6 (2021): C1084-C1085. doi: https://doi.org/10.1152/ajpcell.00393.2021
Tropp N, Gilda JE, Cohen S. Reply to Kissane and Eggington. Am J Physiol Cell Physiol. 2021 Dec 01;321(6):C1084-C1085. doi: 10.1152/ajpcell.00393.2021. PMID: 34874767.
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Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation.

IEEE transactions on medical imaging

Zheng H, Qin Y, Gu Y, Xie F, Yang J, Sun J, Yang GZ.
PMID: 33970858
IEEE Trans Med Imaging. 2021 Sep;40(9):2452-2462. doi: 10.1109/TMI.2021.3078828. Epub 2021 Aug 31.

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background...

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Zheng H, Qin Y, Gu Y, et al. Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation. IEEE Trans Med Imaging. 2021;40(9):2452-2462doi: 10.1109/TMI.2021.3078828.
Zheng, H., Qin, Y., Gu, Y., Xie, F., Yang, J., Sun, J., & Yang, G. Z. (2021). Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation. IEEE transactions on medical imaging, 40(9), 2452-2462. https://doi.org/10.1109/TMI.2021.3078828
Zheng, Hao, et al. "Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation." IEEE transactions on medical imaging vol. 40,9 (2021): 2452-2462. doi: https://doi.org/10.1109/TMI.2021.3078828
Zheng H, Qin Y, Gu Y, Xie F, Yang J, Sun J, Yang GZ. Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation. IEEE Trans Med Imaging. 2021 Sep;40(9):2452-2462. doi: 10.1109/TMI.2021.3078828. Epub 2021 Aug 31. PMID: 33970858.
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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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A Lightweight Fusion Distillation Network for Image Deblurring and Deraining.

Sensors (Basel, Switzerland)

Zhang Y, Liu Y, Li Q, Wang J, Qi M, Sun H, Xu H, Kong J.
PMID: 34450762
Sensors (Basel). 2021 Aug 06;21(16). doi: 10.3390/s21165312.

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass...

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Zhang Y, Liu Y, Li Q, et al. A Lightweight Fusion Distillation Network for Image Deblurring and Deraining. Sensors (Basel). 2021;21(16)doi: 10.3390/s21165312.
Zhang, Y., Liu, Y., Li, Q., Wang, J., Qi, M., Sun, H., Xu, H., & Kong, J. (2021). A Lightweight Fusion Distillation Network for Image Deblurring and Deraining. Sensors (Basel, Switzerland), 21(16), . https://doi.org/10.3390/s21165312
Zhang, Yanni, et al. "A Lightweight Fusion Distillation Network for Image Deblurring and Deraining." Sensors (Basel, Switzerland) vol. 21,16 (2021). doi: https://doi.org/10.3390/s21165312
Zhang Y, Liu Y, Li Q, Wang J, Qi M, Sun H, Xu H, Kong J. A Lightweight Fusion Distillation Network for Image Deblurring and Deraining. Sensors (Basel). 2021 Aug 06;21(16). doi: 10.3390/s21165312. PMID: 34450762; PMCID: PMC8398398.
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Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.

Communications biology

Lu L, Zhou E, Yu W, Chen B, Ren P, Lu Q, Qin D, Lu L, He Q, Tang X, Zhu M, Wang L, Han W.
PMID: 34702997
Commun Biol. 2021 Oct 26;4(1):1225. doi: 10.1038/s42003-021-02758-y.

Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic...

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Lu L, Zhou E, Yu W, et al. Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images. Commun Biol. 2021;4(1):1225doi: 10.1038/s42003-021-02758-y.
Lu, L., Zhou, E., Yu, W., Chen, B., Ren, P., Lu, Q., Qin, D., Lu, L., He, Q., Tang, X., Zhu, M., Wang, L., & Han, W. (2021). Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images. Communications biology, 4(1), 1225. https://doi.org/10.1038/s42003-021-02758-y
Lu, Li, et al. "Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images." Communications biology vol. 4,1 (2021): 1225. doi: https://doi.org/10.1038/s42003-021-02758-y
Lu L, Zhou E, Yu W, Chen B, Ren P, Lu Q, Qin D, Lu L, He Q, Tang X, Zhu M, Wang L, Han W. Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images. Commun Biol. 2021 Oct 26;4(1):1225. doi: 10.1038/s42003-021-02758-y. PMID: 34702997; PMCID: PMC8548495.
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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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Advances in biomedical image processing.

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

Pietka E, Gertych A.
PMID: 33744791
Comput Med Imaging Graph. 2021 Apr;89:101891. doi: 10.1016/j.compmedimag.2021.101891. Epub 2021 Mar 05.

No abstract available.

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Pietka E, Gertych A. Advances in biomedical image processing. Comput Med Imaging Graph. 2021;89:101891doi: 10.1016/j.compmedimag.2021.101891.
Pietka, E., & Gertych, A. (2021). Advances in biomedical image processing. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 89101891. https://doi.org/10.1016/j.compmedimag.2021.101891
Pietka, Ewa, and Gertych, Arkadiusz. "Advances in biomedical image processing." Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 89 (2021): 101891. doi: https://doi.org/10.1016/j.compmedimag.2021.101891
Pietka E, Gertych A. Advances in biomedical image processing. Comput Med Imaging Graph. 2021 Apr;89:101891. doi: 10.1016/j.compmedimag.2021.101891. Epub 2021 Mar 05. PMID: 33744791.
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Showing 553 to 563 of 563 entries