H
Habib Zaidi
Researcher at University Medical Center Groningen
Publications - 557
Citations - 15951
Habib Zaidi is an academic researcher from University Medical Center Groningen. The author has contributed to research in topics: Imaging phantom & Correction for attenuation. The author has an hindex of 62, co-authored 513 publications receiving 13563 citations. Previous affiliations of Habib Zaidi include Johns Hopkins University & University of Southern Denmark.
Papers
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Journal ArticleDOI
COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images
Isaac Shiri,Hossein Arabi,Yazdan Salimi,Amirhossein Sanaat,Azadeh Akhavanallaf,Ghasem Hajianfar,Dariush Askari,Shakiba Moradi,Zahra Mansouri,Masoumeh Pakbin,Saleh Sandoughdaran,Hamid Abdollahi,Amir Reza Radmard,Kiara Rezaei-Kalantari,Mostafa Ghelich Oghli,Habib Zaidi +15 more
TL;DR: In this paper, a deep learning-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images was presented.
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Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging
Yazdan Salimi,Isaac Shiri,Azadeh Akhavanallaf,Zahra Mansouri,Abdollah Saberi Manesh,Amirhossein Sanaat,Masoumeh Pakbin,Dariush Askari,Saleh Sandoughdaran,Ehsan Sharifipour,Hossein Arabi,Habib Zaidi +11 more
TL;DR: In this paper, an automated deep learning-assisted scan range selection technique was developed to reduce radiation dose to patients. But the proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases.
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Current commercial techniques for MRI‐guided attenuation correction are insufficient and will limit the wider acceptance of PET/MRI technology in the clinic
TL;DR: Current techniques implemented on commercial systems for MRAC do not constitute a viable solution and are hampering the wider acceptance of PET/MRI technology in the clinic, but some think that, despite their limitations, these techniques provide adequate correction fulfilling the requirements of the different clinical applications of this technology.
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Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework
Isaac Shiri,Alireza Vafaei Sadr,Mehdi Amini,Yazdan Salimi,Amirhossein Sanaat,Azadeh Akhavanallaf,Behrooz Razeghi,Sohrab Ferdowsi,Abdollah Saberi,Hossein Arabi,Minerva Becker,Sviatoslav Voloshynovskiy,Deniz Gunduz,Arman Rahmim,Habib Zaidi +14 more
TL;DR: Federated DL models could provide robust and generalizable segmentation, while addressing patient privacy and legal and ethical issues in clinical data sharing, and achieve comparable quantitative performance with respect to the centralized DL model.
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The quest for the ideal anato-molecular imaging fusion tool.
TL;DR: The advent of dual-modality PET/CT units is a prominent example of advance in molecular imaging technology and offers the opportunity to modernise the practice of clinical oncology by improving lesion localisation and facilitating treatment planning for radiation therapy.