Zobrazeno 1 - 10 of 169 pro vyhledávání: '"S. Elmahdy"'
1
Akademický článek
Hierarchical Prediction of Registration Misalignment Using a Convolutional LSTM: Application to Chest CT Scans
Autor: Hessam Sokooti, Sahar Yousefi, Mohamed S. Elmahdy, Boudewijn P. F. Lelieveldt, Marius Staring
Publikováno v: IEEE Access, Vol 9, Pp 62008-62020 (2021)
In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: “correct” 0-3 mm, “poor” 3-6
Externí odkaz: https://doaj.org/article/e8560d54a2914d2d8c52e9bea092e98b
Zobrazit plný text záznamu
2
Akademický článek
Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer
Autor: Mohamed S. Elmahdy, Laurens Beljaards, Sahar Yousefi, Hessam Sokooti, Fons Verbeek, Uulke A. Van Der Heide, Marius Staring
Publikováno v: IEEE Access, Vol 9, Pp 95551-95568 (2021)
Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formul
Externí odkaz: https://doaj.org/article/99b9664860a441988bf9b462aafc6f8e
Zobrazit plný text záznamu
3
Akademický článek
Esophageal Tumor Segmentation in CT Images Using a Dilated Dense Attention Unet (DDAUnet)
Autor: Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok, Frank J. W. M. Dankers, Marius Staring
Publikováno v: IEEE Access, Vol 9, Pp 99235-99248 (2021)
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presenc
Externí odkaz: https://doaj.org/article/942e780981834ffb83afb97f284d0649
Zobrazit plný text záznamu
4
Akademický článek
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
Autor: Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus Van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. Van Osch, Elwin De Weerdt, Marius Staring
Publikováno v: IEEE Access, Vol 8, Pp 204825-204838 (2020)
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data,
Externí odkaz: https://doaj.org/article/723dd39f480f476b9cf7a6eac71a4db7
Zobrazit plný text záznamu
5
Esophageal Tumor Segmentation in CT Images Using a Dilated Dense Attention Unet (DDAUnet)
Autor: Mohamed S. Elmahdy, Sahar Yousefi, I.M. Lips, Marius Staring, Roel T. Zinkstok, Frank J. W. M. Dankers, Hessam Sokooti, Mohammad Taghi Manzuri Shalmani
Publikováno v: IEEE Access, Vol 9, Pp 99235-99248 (2021)
IEEE Access, 9, 99235-99248. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IEEE Access, 9
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presenc
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d5e0a2805a6d2ea401f4800d0a4a70b
https://ieeexplore.ieee.org/document/9481104/
Zobrazit plný text záznamu
7
Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM: Application to Chest CT Scans
Autor: Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Marius Staring, Boudewijn P. F. Lelieveldt
Publikováno v: IEEE Access, 9
IEEE Access, Vol 9, Pp 62008-62020 (2021)
IEEE Access, 9, 62008-62020. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: “correct” 0-3 mm, “poor” 3-6
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1fe0ae16b9c256c7aafe2afb59a229d
http://resolver.tudelft.nl/uuid:50ce781d-296c-4ea3-baf0-5bb7add3cc28
Zobrazit plný text záznamu
8
Joint registration and segmentation via multi-task learning for adaptive radiotherapy of prostate cancer
Autor: Marius Staring, Hessam Sokooti, Uulke A. van der Heide, Mohamed S. Elmahdy, Fons J. Verbeek, Sahar Yousefi, Laurens Beljaards
Publikováno v: IEEE Access, 9, 95551-95568. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IEEE Access, Vol 9, Pp 95551-95568 (2021)
IEEE Access, 9, 95551-95568. Institute of Electrical and Electronics Engineers (IEEE)
Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formul
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49087a3c43c3b2876b41d1591fad5df6
https://hdl.handle.net/1887/3277538
Zobrazit plný text záznamu
10
An adaptive intelligence algorithm for under sampled knee MRI reconstruction
Autor: Jeroen Hendrikus Fransiscus Van Gemert, Elwin de Weerdt, Tim Nielsen, Mohamed S. Elmahdy, Boudewijn P. F. Lelieveldt, Sahar Yousefi, Nicola Pezzotti, Mariya Doneva, Christophe Schuelke, Marius Staring, Matthias J.P. van Osch, Sergey Kastryulin
Publikováno v: IEEE Access, 8, 204825-204838. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IEEE Access, Vol 8, Pp 204825-204838 (2020)
IEEE Access, 8
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data,
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ffbddabfb16b8e854bce8a89aa63fd
http://hdl.handle.net/1887/3184247
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje