Research Projects

Teeth segmentation in dental imaging is a critical task in various dental practices, including diagnosis, treatment planning, and orthodontic procedures. It involves extracting the anatomical boundaries of teeth from 2D or 3D images, such as X-rays, CT scans, or intraoral photographs. Accurate segmentation allows for precise analysis, enabling better treatment decisions and improving patient outcomes. However, current methods often struggle with segmentation in complex cases, such as overlapping teeth, dental restorations, or images with poor resolution, making this area an essential focus for further research. Scientific Objectives: The primary scientific objectives of this research are: • To develop a robust and automated method for accurately segmenting individual teeth from dental images (both 2D and 3D). • To enhance the performance of existing segmentation models, especially in challenging scenarios like distorted images or varying tooth shapes and alignments. • To evaluate and compare the performance of deep learning algorithms (such as convolutional neural networks) against traditional segmentation techniques in terms of accuracy, speed, and robustness. Hypothesis to be tested: We hypothesize that deep learning models, particularly Convolutional Neural Networks (CNNs) and their variants (e.g., U-Net, Mask R-CNN), will significantly outperform traditional image processing techniques for teeth segmentation, particularly in complex or low-quality images. The model will be tested on a diverse set of dental images to determine its generalization ability, robustness, and precision in extracting the correct boundaries of each tooth. Main Experiments to be Carried Out: • Data Collection: Assemble a large and diverse dataset of dental images, including X-rays, CT scans, and intraoral photos, with varying qualities, tooth types, and conditions (e.g., healthy teeth, dental restorations, misaligned teeth). • Model Development: Implement and train several deep learning-based segmentation models (e.g., U-Net, DeepLabV3, Mask R-CNN) using annotated datasets for training and validation. • Comparison with Traditional Methods: Evaluate the performance of the deep learning models against traditional methods, using accuracy metrics (e.g., dice coefficient, intersection over union). • Evaluation on Complex Cases: Test the models on challenging cases, such as teeth with significant overlaps, dental implants, or images with low contrast and resolution. Significance to the Field of Research: The successful development of an advanced teeth segmentation method has the potential to revolutionize dental imaging analysis by providing: • Improved accuracy and efficiency in automated dental diagnosis and treatment planning. • Automated systems for large-scale dental data processing, leading to faster diagnostics in clinics and hospitals. • Cost-effectiveness in dental imaging analysis, reducing the reliance on manual labor and the potential for human error.