3D Slicer: Revolutionizing Medical Imaging Education with AI

Artificial Intelligence (AI) is transforming medical imaging, impacting image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, is a valuable educational tool due to its AI integration. It allows students to gain hands-on experience with diverse medical images and AI, reinforcing their understanding of anatomy and imaging technology. This enhances independent learning and clinical reasoning, preparing skilled professionals for clinical practice, research, and technological innovation. The application of AI algorithms in medical image processing facilitates their translation from the lab to practical clinical applications and education.

This review examines theories and research from the last 5 years, focusing on peer-reviewed content, including meta-analyses and review papers. The PubMed database was thoroughly explored for biomedicine-related journals, and Web of Science was searched using relevant keywords. Search queries included “applications” AND “3D Slicer” AND “medical” and “applications” AND “3D Slicer” AND “education”. Inclusion criteria included peer-reviewed articles and research published in 2019 or later, while exclusion criteria included conference poster papers and studies not written in English. Key characteristics of interest were identified to ensure reliable information extraction and research synthesis. The methodology involved coding, data entry, and data management, with regular group meetings to discuss potential modifications.

3D Slicer is a crucial medical image analysis platform, offering potential in medical imaging education for image segmentation, reconstruction, computer-aided diagnosis, research, and quantitative analysis. It supports various medical image formats like DICOM, NIfTI, and NRRD, transforming images into 3D models. The platform performs image processing, segmentation, registration, and analysis, with over a hundred open-source extensions for radiomics analysis, AI-based organ segmentation, surgical navigation, and radiation therapy tools. Its extensive functionality surpasses clinical workstations, bridging the gap between pedagogy and clinical practice.

Compared to traditional teaching methods, 3D Slicer demonstrates great potential in medical imaging education. Unlike commercial software, it eliminates the need for costly licenses and specialized hardware. The platform offers image examples coupled with open-source image data. Educators can organize lessons effectively, providing anonymized image examples for students to conduct image readings, generate 3D images, and construct models. This engages students' enthusiasm, encouraging them to seek solutions and solve problems. Students with programming skills can further develop and optimize the software, improving their practical and innovative abilities. This integrates medical imaging education theory with practice, rendering images visible, tangible, and functional.

3D Slicer is a powerful tool for segmentation and reconstruction. Medical images contain complex details, and image segmentation techniques effectively separate and label tissues, organs, or lesion areas, providing clearer visual information. This helps medical students learn and understand anatomical structures and disease characteristics. Image reconstruction further enhances understanding by creating 3D models from 2D images, allowing students to visualize complex anatomical relationships. Hadi et al. [36] and Bindschadler et al. [37] highlight the platform's capabilities in this area, emphasizing its role in improving anatomical knowledge.

3D Slicer facilitates computer-aided diagnosis by providing tools for analyzing medical images and identifying potential abnormalities. AI algorithms integrated into the platform can assist in detecting subtle patterns and features that might be missed by the human eye. This enhances diagnostic accuracy and efficiency. In research, 3D Slicer supports quantitative analysis, statistical modeling, and the development of new diagnostic techniques. The platform's open-source nature encourages collaboration and innovation, allowing researchers to share tools and methodologies.

Quantitative analysis is a critical aspect of medical imaging, enabling precise measurements and assessments of anatomical structures and pathological changes. 3D Slicer provides tools for measuring volumes, distances, and other parameters, allowing for objective evaluation of disease progression and treatment response. This capability is particularly valuable in clinical trials and research studies, where accurate and reproducible measurements are essential. The platform's ability to perform quantitative analysis enhances the objectivity and reliability of medical imaging assessments.

3D Slicer offers several advantages over traditional medical imaging education methods. Its open-source nature eliminates the need for expensive software licenses, making it accessible to a wider range of students and institutions. The platform's flexibility and extensibility allow for customization to meet specific educational needs. Interactive learning techniques, such as case-based learning and virtual simulations, can be integrated into the curriculum, enhancing student engagement and comprehension. Collaboration between medical and computer science faculties is facilitated, fostering a better understanding of both fields and encouraging innovation in AI-based medical imaging.

Despite its advantages, 3D Slicer also presents challenges. The learning curve can be steep for students unfamiliar with medical imaging software and AI algorithms. The platform's complexity may require significant training and support. Data security and privacy concerns must be addressed when using patient data for educational purposes. Ensuring the accuracy and reliability of AI-based analysis tools is also crucial. Overcoming these challenges requires careful planning, effective training, and adherence to ethical guidelines.

3D Slicer is a valuable tool in medical imaging education, offering potential in image segmentation, reconstruction, diagnosis, and quantitative analysis. Its open-source nature, flexibility, and AI integration make it a cost-effective and innovative educational platform. By addressing the challenges and limitations, educators can effectively utilize 3D Slicer to enhance student learning and prepare skilled professionals for the future of medical imaging.