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pro vyhledávání: '"Bénard, François"'
Prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging provides a tremendously exciting frontier in visualization of prostate cancer (PCa) metastatic lesions. However, accurate segmentation of meta
Externí odkaz:
http://arxiv.org/abs/2407.18555
Publikováno v:
Physica Medica: European Journal of Medical Physics, 2024
The purpose was to investigate the spatial heterogeneity of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) uptake within parotid glands. We aim to quantify patterns in well-defined regions to facilitate further investiga
Externí odkaz:
http://arxiv.org/abs/2401.02496
Autor:
Ahamed, Shadab, Xu, Yixi, Gowdy, Claire, O, Joo H., Bloise, Ingrid, Wilson, Don, Martineau, Patrick, Bénard, François, Yousefirizi, Fereshteh, Dodhia, Rahul, Lavista, Juan M., Weeks, William B., Uribe, Carlos F., Rahmim, Arman
This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images. These networks were trained, validated, and tested on a diverse, multi-in
Externí odkaz:
http://arxiv.org/abs/2311.09614
Xerostomia and radiation-induced salivary gland dysfunction remain a common side effect for head-and-neck radiotherapy patients, and attempts have been made to quantify the heterogeneous dose response within parotid glands. Here several models of par
Externí odkaz:
http://arxiv.org/abs/2309.09402
Autor:
Sample, Caleb, Rahmim, Arman, Uribe, Carlos, Bénard, François, Wu, Jonn, Fedrigo, Roberto, Clark, Haley
Objective: To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution. Approach: Blind deconvolution is a method of estimating the hypothetical "deblur
Externí odkaz:
http://arxiv.org/abs/2309.00590
Autor:
Yousefirizi, Fereshteh, Shiri, Isaac, O, Joo Hyun, Bloise, Ingrid, Martineau, Patrick, Wilson, Don, Bénard, François, Sehn, Laurie H., Savage, Kerry J., Zaidi, Habib, Uribe, Carlos F., Rahmim, Arman
The time-consuming task of manual segmentation challenges routine systematic quantification of disease burden. Convolutional neural networks (CNNs) hold significant promise to reliably identify locations and boundaries of tumors from PET scans. We ai
Externí odkaz:
http://arxiv.org/abs/2212.09908
Autor:
Wharton, Luke, McNeil, Scott W., Zhang, Chengcheng, Engudar, Gokce, Van de Voorde, Michiel, Zeisler, Jutta, Koniar, Helena, Sekar, Sathiya, Yuan, Zheliang, Schaffer, Paul, Radchenko, Valery, Ooms, Maarten, Kunz, Peter, Bénard, François, Yang, Hua
Publikováno v:
In Nuclear Medicine and Biology September-October 2024 136-137
Publikováno v:
In Physica Medica May 2024 121
Autor:
Bendre, Shreya, Merkens, Helen, Kuo, Hsiou-Ting, Ng, Pauline, Wong, Antonio A.W.L., Lau, Wing Sum, Zhang, Zhengxing, Kurkowska, Sara, Chen, Chao-Cheng, Uribe, Carlos, Bénard, François, Lin, Kuo-Shyan
Publikováno v:
In European Journal of Medicinal Chemistry 15 March 2024 268
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