Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Peter van Ooijen"'
Autor:
Nikos Sourlos, Rozemarijn Vliegenthart, Joao Santinha, Michail E. Klontzas, Renato Cuocolo, Merel Huisman, Peter van Ooijen
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Various healthcare domains have witnessed successful preliminary implementation of artificial intelligence (AI) solutions, including radiology, though limited generalizability hinders their widespread adoption. Currently, most research group
Externí odkaz:
https://doaj.org/article/7bd1a19456934e9b9fa9aa090ad2aa7d
Autor:
Salvatore Gitto, Renato Cuocolo, Merel Huisman, Carmelo Messina, Domenico Albano, Patrick Omoumi, Elmar Kotter, Mario Maas, Peter Van Ooijen, Luca Maria Sconfienza
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Objective To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which i
Externí odkaz:
https://doaj.org/article/12d13c803a7f4dc689fe1bccf51755c0
Autor:
Burak Kocak, Tugba Akinci D’Antonoli, Nathaniel Mercaldo, Angel Alberich-Bayarri, Bettina Baessler, Ilaria Ambrosini, Anna E. Andreychenko, Spyridon Bakas, Regina G. H. Beets-Tan, Keno Bressem, Irene Buvat, Roberto Cannella, Luca Alessandro Cappellini, Armando Ugo Cavallo, Leonid L. Chepelev, Linda Chi Hang Chu, Aydin Demircioglu, Nandita M. deSouza, Matthias Dietzel, Salvatore Claudio Fanni, Andrey Fedorov, Laure S. Fournier, Valentina Giannini, Rossano Girometti, Kevin B. W. Groot Lipman, Georgios Kalarakis, Brendan S. Kelly, Michail E. Klontzas, Dow-Mu Koh, Elmar Kotter, Ho Yun Lee, Mario Maas, Luis Marti-Bonmati, Henning Müller, Nancy Obuchowski, Fanny Orlhac, Nikolaos Papanikolaou, Ekaterina Petrash, Elisabeth Pfaehler, Daniel Pinto dos Santos, Andrea Ponsiglione, Sebastià Sabater, Francesco Sardanelli, Philipp Seeböck, Nanna M. Sijtsema, Arnaldo Stanzione, Alberto Traverso, Lorenzo Ugga, Martin Vallières, Lisanne V. van Dijk, Joost J. M. van Griethuysen, Robbert W. van Hamersvelt, Peter van Ooijen, Federica Vernuccio, Alan Wang, Stuart Williams, Jan Witowski, Zhongyi Zhang, Alex Zwanenburg, Renato Cuocolo
Publikováno v:
Insights into Imaging, Vol 15, Iss 1, Pp 1-18 (2024)
Abstract Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods We conducted an online modified Delphi study with a group of international experts.
Externí odkaz:
https://doaj.org/article/84735d3b21f949ab8a02d96fb603cc0c
Autor:
Jingxuan Wang, Nikos Sourlos, Sunyi Zheng, Nils van der Velden, Gert Jan Pelgrim, Rozemarijn Vliegenthart, Peter van Ooijen
Publikováno v:
Heliyon, Vol 9, Iss 6, Pp e17104- (2023)
Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to ima
Externí odkaz:
https://doaj.org/article/4a4216e127174933865f8f081aec9ad6
Autor:
Andrea Ponsiglione, Arnaldo Stanzione, Gaia Spadarella, Agah Baran, Luca Alessandro Cappellini, Kevin Groot Lipman, Peter Van Ooijen, Renato Cuocolo
Publikováno v:
European Radiology. SPRINGER
Objective To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. Methods Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::703310e7f323d2a980561cfe45ede888
https://doi.org/10.1007/s00330-022-09180-w
https://doi.org/10.1007/s00330-022-09180-w
Autor:
Dewinda Julianensi Rumala, Peter van Ooijen, Reza Fuad Rachmadi, Anggraini Dwi Sensusiati, I Ketut Eddy Purnama
Publikováno v:
Journal of Digital Imaging.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::737e7c268c05874fbaa46f72f94963d8
https://doi.org/10.1007/s10278-023-00828-7
https://doi.org/10.1007/s10278-023-00828-7
Autor:
Hung Chu, Luis Ricardo De la O Arévalo, Wei Tang, Baoqiang Ma, Yan Li, Alessia De Biase, Stefan Both, Johannes Albertus Langendijk, Peter van Ooijen, Nanna Maria Sijtsema, Lisanne V. van Dijk
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031274190
Delineation of Gross Tumor Volume (GTV) is essential for the treatment of cancer with radiotherapy. GTV contouring is a time-consuming specialized manual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39d56161d34f3a3980b70c75e7b5ee34
https://doi.org/10.1007/978-3-031-27420-6_12
https://doi.org/10.1007/978-3-031-27420-6_12
Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification
Publikováno v:
Cancers. 14(16)
Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd3802ce2980f5b1837246c397bd004d
https://pubmed.ncbi.nlm.nih.gov/36010861
https://pubmed.ncbi.nlm.nih.gov/36010861
Autor:
Yeshaswini Nagaraj, Hendrik Joost Wisselink, Mieneke Rook, Jiali Cai, Sunil Belur Nagaraj, Grigory Sidorenkov, Raymond Veldhuis, Matthijs Oudkerk, Rozemarijn Vliegenthart, Peter van Ooijen
Publikováno v:
Journal of Digital Imaging, 35, 538-550. Springer
JOURNAL OF DIGITAL IMAGING, 35, 538-550. SPRINGER
JOURNAL OF DIGITAL IMAGING, 35, 538-550. SPRINGER
The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::207730f9e8e6dd643ce7a409954822c7
https://research.utwente.nl/en/publications/0e0c6409-2556-41f4-8c6a-c27041dec0d2
https://research.utwente.nl/en/publications/0e0c6409-2556-41f4-8c6a-c27041dec0d2