Optimizing DICOM File Processing: A Comprehensive Workflow for AI and 3D Printing in Medicine
10.14316/pmp.2024.35.4.106
- Author:
Dong Hyeok CHOI
1
;
Jin Sung KIM
;
So Hyun AHN
Author Information
1. Department of Medicine, Yonsei University College of Medicine, Seoul, Korea
- Publication Type:Original Article
- From:
Progress in Medical Physics
2024;35(4):106-115
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies.
Methods:The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration.
Results:The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible.
Conclusions:This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.
