Prog Med Phys.  2024 Dec;35(4):106-115. 10.14316/pmp.2024.35.4.106.

Optimizing DICOM File Processing: A Comprehensive Workflow for AI and 3D Printing in Medicine

• Choi DH
• Kim JS
• Ahn SH

• KMID: 2564149
• DOI: http://doi.org/10.14316/pmp.2024.35.4.106

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 study incorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility with virtual 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.

Keyword

DICOM preprocessing; AI in medical imaging; 3D printing; Metaverse applications

Figure

• 

  Fig. 1 Pipeline of the CT and RT structure DICOM file preprocessing. CT, computed tomography; RT, radiotherapy; DICOM, Digital Imaging and Communications in Medicine; NIfTI, Neuroimaging Informatics Technology Initiative; STL, Stereolithography; PLY, polygon file format.

• 

  Fig. 2 CT and radiotherapy structure of the patient preprocessed with Neuroimaging Informatics Technology Initiative. (a, c) show the results of the preprocessed CT data, while (b, d) display the corresponding structure information for slices (a) and (c), respectively. Specifically, (b) shows the segmented left and right breasts, and (d) shows the segmented liver and spleen. CT, computed tomography.

• 

  Fig. 3 CT and RT structure of the patient preprocessed with STL. (a) presents the CT data converted into the STL format, while (b) shows the corresponding RT structure information converted into the STL format. The organs shown in (b) are the breasts, liver, and spleen. CT, computed tomography; STL, Stereolithography; RT, radiotherapy.

Reference

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