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pro vyhledávání: '"Uribe, Carlos F."'
Towards the need for automated and precise AI-based analysis of medical images, we present RT-utils, a specialized Python library tuned for the manipulation of radiotherapy (RT) structures stored in DICOM format. RT-utils excels in converting the pol
Externí odkaz:
http://arxiv.org/abs/2405.06184
Autor:
Ahamed, Shadab, Xu, Yixi, Bloise, Ingrid, O, Joo H., Uribe, Carlos F., Dodhia, Rahul, Ferres, Juan L., Rahmim, Arman
Publikováno v:
Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641Q (3 April 2023)
Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians a
Externí odkaz:
http://arxiv.org/abs/2403.07105
Autor:
Ahamed, Shadab, Dubljevic, Natalia, Bloise, Ingrid, Gowdy, Claire, Martineau, Patrick, Wilson, Don, Uribe, Carlos F., Rahmim, Arman, Yousefirizi, Fereshteh
Publikováno v:
Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323M (4 April 2022)
Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of
Externí odkaz:
http://arxiv.org/abs/2403.07092
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
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:
Yousefirizi, Fereshteh, Dubljevic, Natalia, Ahamed, Shadab, Bloise, Ingrid, Gowdy, Claire, O, Joo Hyun, Farag, Youssef, de Schaetzen, Rodrigue, Martineau, Patrick, Wilson, Don, Uribe, Carlos F., Rahmim, Arman
Segmentation of lymphoma lesions is challenging due to their varied sizes and locations in whole-body PET scans. This work presents a fully-automated segmentation technique using a multi-center dataset of diffuse large B-cell lymphoma (DLBCL) with he
Externí odkaz:
http://arxiv.org/abs/2208.00274
Autor:
Fedrigo, Roberto, Segars, William P., Martineau, Patrick, Gowdy, Claire, Bloise, Ingrid, Uribe, Carlos F., Rahmim, Arman
Purpose: The XCAT phantom allows for highly sophisticated multimodality imaging research. It includes a complete set of organs, muscle, bone, soft tissue, while also accounting for age, sex, and body mass index (BMI), which allows phantom studies to
Externí odkaz:
http://arxiv.org/abs/2107.11429
Publikováno v:
In Computer Methods and Programs in Biomedicine March 2024 245
Autor:
Xu, Yixi, Klyuzhin, Ivan, Harsini, Sara, Ortiz, Anthony, Zhang, Shun, Bénard, François, Dodhia, Rahul, Uribe, Carlos F., Rahmim, Arman, Lavista Ferres, Juan
Publikováno v:
In Computers in Biology and Medicine May 2023 158
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