Development of a 3D imaging platform for automated anatomical Variation analysis

Thesis Abstract

The accurate delineation and analysis of anatomical variations are critical for advancing personalized medicine, preoperative planning, and anatomical research; however, traditional methods involving manual measurement and qualitative assessment are time-consuming, operator-dependent, and often limited in scale and precision. This study aims to develop an innovative, automated 3D imaging platform capable of high-throughput, precise analysis of anatomical variations across diverse populations. The specific objectives are to design and implement a robust software framework for 3D visualization, develop algorithms for automated landmark detection and measurement, validate the platform's accuracy against manual gold-standard assessments, and evaluate its effectiveness in identifying clinically relevant anatomical variations across different demographic groups. The research adopts a mixed-methods design, integrating quantitative validation techniques with qualitative usability assessments. The study population comprises a sample of 200 anonymized high-resolution computed tomography (CT) scans sourced from a publicly accessible medical imaging database, representing a diverse demographic profile in terms of age, sex, and ethnicity. A stratified random sampling approach ensures representative sampling across these variables. The primary data collection instruments include the developed imaging platform, standardized manual measurement protocols for validation, and user feedback questionnaires. The platform employs state-of-the-art computer vision techniques, including convolutional neural networks for landmark detection and 3D mesh analysis algorithms, integrated into an open-source software environment developed using Python and C++. Data analysis begins with assessing the platform's accuracy by comparing automated measurements to manual gold standards using paired t-tests and Bland-Altman plots, with the threshold for agreement set at 95% confidence interval. The reliability of automated landmark detection is evaluated via intra- and inter-observer consistency metrics, including intraclass correlation coefficients (ICC). The platform's capability to classify anatomical variations is examined using machine learning classifiers, such as support vector machines, with performance metrics including accuracy, sensitivity, and specificity. Additionally, qualitative data from user feedback are analyzed through thematic content analysis to assess usability, interface design, and potential workflow integration. Expected findings indicate that the developed platform will demonstrate statistically significant agreement with manual measurements, exhibiting ICCs above 0.90, and high classification accuracy (above 85%) in identifying key anatomical variants. The platform is anticipated to substantially reduce analysis time by at least 70% compared to manual methods and enhance reproducibility. It is also expected to receive favorable usability ratings from clinicians and researchers, emphasizing its potential for routine clinical and research applications. This study contributes novel insights into the integration of advanced computer vision and machine learning techniques within a unified, user-friendly 3D imaging platform tailored for anatomical variation analysis. It addresses critical gaps in existing literature regarding automation, scalability, and validity of digital anatomical assessments, providing a foundation for future developments in personalized anatomical modeling, surgical planning, and large-scale epidemiological studies. The study concludes that the proposed platform represents a significant technological advancement with the potential to transform anatomical research and clinical practice by enabling rapid, accurate, and standardized analysis of anatomical variations across large datasets. Recommendations include further refinement through integration with radiology information systems, expansion to other imaging modalities, and validation across additional anatomical regions to enhance its versatility and clinical utility. Future research should explore longitudinal applications, integration with virtual reality environments, and potential customization for educational purposes.