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Congress: ECR24
Poster Number: C-21327
Type: EPOS Radiologist (educational)
DOI: 10.26044/ecr2024/C-21327
Authorblock: F. Buemi, C. Giardina, A. Perri, S. Caloggero, A. Celona, N. Sicilia, O. Ventura Spagnolo, F. Galletta, G. Mastroeni; Messina/IT
Disclosures:
Francesco Buemi: Other: supported by the group "Bracco imaging S.p.A"
Claudio Giardina: Other: supported by the group "Bracco imaging S.p.A"
Alessandro Perri: Other: supported by the group "Bracco imaging S.p.A"
Simona Caloggero: Other: supported by the group "Bracco imaging S.p.A"
Antonio Celona: Other: supported by the group "Bracco imaging S.p.A"
Nunziella Sicilia: Other: supported by the group "Bracco imaging S.p.A"
Orazio Ventura Spagnolo: Other: supported by the group "Bracco imaging S.p.A"
Fabio Galletta: Other: supported by the group "Bracco imaging S.p.A"
Giampiero Mastroeni: Other: supported by the group "Bracco imaging S.p.A"
Keywords: Artificial Intelligence, CT, MR, Computer Applications-3D, Computer Applications-Detection, diagnosis, Segmentation, Education and training
Learning objectives To serve as a user-friendly and basic introduction to MONAl Label specifically tailored for radiologists who do not possess a deep understanding of artificial intelligence (Al) but are keen to explore its applications. To demonstrate the functionality of MONAI Label and its practical applications through 3D-Slicer. To serve as a simple and fundamental introduction to MONAI Label for radiologists. For more in-depth and technical information, we will provide relevant references. 
Read more Background One of the most significant challenges that practicing radiologists face when adopting Al is how to integrate it into their daily practice and contribute to its development. Despite the availability of numerous software, web-based platforms, and services, they are often costly and out of reach for financially constrained healthcare institutions. Conversely, open-source tools are frequently challenging to utilize due to their requirement for knowledge that extends beyond the typical expertise of radiologists. Furthermore, segmentation tasks frequently entail laborious and time-consuming...
Read more Findings and procedure details Steps to install MONAI label and activate the serverInstallation requirements [fig 2] MONAI label can be installed through three methods: from PyPI, GitHub, and DockerHub. [fig 3] PyPI (Python Package Index), is an online repository for sharing and distributing Python software packages, allowing developers to easily publish, discover, and install Python libraries and modules using the pip tool. We have installed MONAI label from PyPI, using the following steps. 1) Install AnacondaAnaconda is an open-source software that makes it easier for people to work with Python, especially...
Read more Conclusion MONAI Label integrated with 3D-Slicer allows radiologists to easily perform image segmentation, facilitating the application of AI in their practice and research settings.
Read more References 1. Fedorov A. et al (2012). 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic resonance imaging 30, 1323–1341.2. Diaz-Pinto A. et al (2023) MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images. arXiv:2203.12362 [cs.HC].3. Diaz-Pinto A. et al (2023). DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. arXiv:2305.10655 [eess.IV]4. Ronneberger et al (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 [cs.CV]5. Nath V. et al (2021) Diminishing Uncertainty...
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