DICOM segmentation is a specialized medical imaging technique that classifies pixels in medical images using the DICOM standard. Medical professionals have multiple options for storing and managing segmentation data, primarily through DICOM SEG and DICOM RTStruct formats. Each format serves specific purposes in the medical imaging workflow, offering distinct advantages for different use cases.
The medical imaging community has evolved to support multiple approaches to storing segmentation data. While both DICOM SEG and DICOM RTStruct serve the purpose of representing segmented regions in medical images, they were developed to address different clinical needs and workflows. Understanding their differences is crucial for implementing the right solution for specific medical imaging requirements.
DICOM segmentation can be implemented through two primary formats, each with its own strengths:
Also Read: What is DICOM? The Complete Guide to Medical Imaging Standards
The landscape of medical image segmentation has evolved significantly with the introduction of advanced tools and platforms. These solutions combine the power of traditional DICOM formats with modern user interfaces and AI-assisted capabilities. One notable example is the Smart Paint tool by Collective Minds Research, which has revolutionized the interactive segmentation process for medical images.
Smart Paint represents a significant advancement in medical imaging technology, offering an intuitive approach to segmentation that works with the RTStruct format for it's wide compatibility with legacy and open source software. The tool was notably utilized in the Horizon 2020 EuCanImage project, demonstrating its capability to handle large-scale segmentation tasks across thousands of medical examinations.
Also Read: DICOM Metadata Extraction: A Comprehensive Guide for Medical Imaging Professionals
When implementing DICOM segmentation in clinical workflows, several factors should be considered:
Also Read: DICOM Modalities: A Comprehensive Guide to Medical Imaging Technologies
When working with DICOM segmentation:
Choose the appropriate format based on:
Consider hybrid approaches:
Ensure proper validation:
"Radiologists in the hospital need to visualize and explore the images, identify where the
tumors and the lesions are, and label them. We face a problem of different vendors, different protocols, different scanners, different imaging contrast media, for the huge dataset of almost 25,000 Patients."Dr. Karim Lekadir is the Director of the Artificial Intelligence in Medicine Lab at the Universitat de Barcelona (BCN-AIM) and the Project Coordinator of EuCanImage
DICOM segmentation, whether implemented through SEG or RTStruct formats, provides essential functionality for medical image analysis. The choice between formats depends on specific clinical needs, system requirements, and workflow considerations. Modern tools like Smart Paint demonstrate how these traditional formats can be enhanced with contemporary user interfaces and AI assistance, improving both efficiency and accuracy in medical image analysis.
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The choice depends on your specific use case. Choose DICOM SEG for complex 3D segmentations and AI workflows, and RTStruct for radiation therapy planning or when working with legacy systems.
Yes, conversion between formats is possible, though some information may be lost due to the different ways each format represents segmentation data.
Smart Paint provides an intuitive interface for creating segmentations that can be saved in either DICOM SEG or RTStruct format, depending on the clinical requirements.
RTStruct typically requires less storage space as it stores contours, while DICOM SEG stores full volumetric data, resulting in larger file sizes but more detailed representation.
Reviewed by: Mathias Engström on November 14, 2024