Development of an AI-based 3D Anatomical Structure Segmentation Tool Using Medical Imaging

Thesis Abstract

Accurate segmentation of three-dimensional (3D) anatomical structures from medical imaging remains a critical challenge in diagnostic radiology, surgical planning, and biomedical research, owing to the complexity, variability, and volume of medical image data. Traditional segmentation methods, including manual delineation and basic algorithmic approaches, are often time-consuming, subjective, and limited in handling the intricate spatial relationships inherent in 3D imaging modalities such as MRI and CT scans. This study aims to develop an advanced artificial intelligence (AI)-based segmentation tool leveraging deep learning algorithms to automatically delineate 3D anatomical structures with high precision and efficiency. The specific objectives include designing a convolutional neural network (CNN) architecture optimized for volumetric data, training and validating the model on a comprehensive dataset of anonymized MRI and CT scans, and evaluating its performance against existing segmentation techniques in terms of accuracy, computational efficiency, and robustness. The research adopts a quantitative, experimental design within a positivist paradigm. The primary population comprises 1,200 de-identified medical images sourced from publicly available repositories and hospital cohorts, randomly divided into training, validation, and testing subsets at ratios of 70%, 15%, and 15%, respectively. A stratified sampling technique ensures representation across different anatomical regions and image acquisition parameters. Data collection involved pre-processing of images through normalization, annotation of ground-truth masks by expert radiologists, and encoding of data into 3D volumetric formats suitable for deep learning models. The core instrument comprises a bespoke CNN architecture inspired by the U-Net and V-Net frameworks, with hyperparameter tuning via grid search to optimize segmentation performance. Validity and reliability are ensured through cross-validation and inter-rater agreement measures, such as Cohen’s kappa, for ground-truth annotations. The analysis employs advanced image processing and machine learning methodologies, including the implementation of the proposed 3D CNN model within TensorFlow and Keras frameworks. Model performance is evaluated using quantitative metrics such as Dice similarity coefficient, Jaccard index, precision, recall, and Hausdorff distance. Additionally, model robustness is assessed through stress testing with images exhibiting varying noise levels and artifacts. Comparative analysis involves statistical tests, including paired t-tests and ANOVA, to determine significant differences in segmentation accuracy between the proposed AI model and conventional semi-automated or manual segmentation approaches. The theoretical underpinning draws on the Hierarchical Recognition Theory to explain the model's capacity for feature extraction and the Spatial Attention Theory to justify architecture enhancements aimed at improving model focus on relevant anatomical features. Expected findings anticipate that the developed AI tool will outperform existing segmentation techniques, achieving a Dice coefficient exceeding 0. ninth 85 across diverse anatomical datasets, with a substantial reduction in processing time. The model is projected to demonstrate high generalizability, with robustness maintained under varied imaging conditions. The study’s contributions to knowledge include providing a validated, scalable deep learning framework for 3D anatomical segmentation that integrates state-of-the-art AI methodologies with clinical applicability, thereby advancing precision medicine, improving diagnostic workflows, and facilitating biomedical research. In conclusion, the developed AI-based segmentation tool offers a significant technological advancement that addresses current limitations in 3D medical image analysis. The study recommends further validation with prospective datasets, integration into clinical decision-support systems, and exploration of real-time segmentation capabilities. Future research avenues suggested include expanding the model to multi-modal imaging data, incorporating explainability features to enhance interpretability, and investigating transfer learning techniques to accelerate deployment across different anatomical regions. Overall, this research underscores the transformative potential of AI-driven solutions in augmenting medical imaging analysis and improving patient care outcomes.