A Tooth Segmentation Method Based on Multiple Geometric Feature Learning

Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder-Decoder Network

BACKGROUND

Dental disorders are one of the most important health problems, affecting billions of people all over the world. Early diagnosis is important for effective treatment planning. Precise dental disease segmentation requires reliable tooth numbering, which may be prone to errors if performed manually. These steps can be automated using artificial intelligence, which may provide fast and accurate results. Among the AI methodologies, deep learning has recently shown excellent performance in dental image processing, allowing effective tooth segmentation and numbering.

METHODS

This paper proposes the Squeeze and Excitation Inception Block-based Encoder-Decoder (SE-IB-ED) network for teeth segmentation in panoramic X-ray images. It combines the InceptionV3 model for encoding with a custom decoder for feature integration and segmentation, using pointwise convolution and an attention mechanism. A dataset of 313 panoramic radiographs from private clinics was annotated using the Fédération Dentaire Internationale (FDI) system. PSPL and SAM augmented the annotation precision and effectiveness, with SAM automating teeth labeling and subsequently applying manual corrections.

RESULTS

The proposed SE-IB-ED network was trained and tested using 80% training and 20% testing of the dataset, respectively. Data augmentation techniques were employed during training. It outperformed the state-of-the-art models with a very high F1-score of 92.65%, mIoU of 86.38%, and 92.84% in terms of accuracy, precision of 92.49%, and recall of 99.92% in the segmentation of teeth.

CONCLUSIONS

According to the results obtained, the proposed method has great potential for the accurate segmentation of all teeth regions and backgrounds in panoramic X-ray images.

Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs

OBJECTIVES

This research focuses on performing teeth segmentation with panoramic radiograph images using a denoised encoder-based residual U-Net model, which enhances segmentation techniques and has the capacity to adapt to predictions with different and new data in the dataset, making the proposed model more robust and assisting in the accurate identification of damages in individual teeth.

METHODS

The effective segmentation starts with pre-processing the Tufts dataset to resize images to avoid computational complexities. Subsequently, the prediction of the defect in teeth is performed with the denoised encoder block in the residual U-Net model, in which a modified identity block is provided in the encoder section for finer segmentation on specific regions in images, and features are identified optimally. The denoised block aids in handling noisy ground truth images effectively.

RESULTS

Proposed module achieved greater values of mean dice and mean IoU with 98.90075 and 98.74147 CONCLUSIONS: Proposed AI enabled model permitted a precise approach to segment the teeth on Tuffs dental dataset in spite of the existence of densed dental filling and the kind of tooth.

CLINICAL SIGNIFICANCE

The proposed model is pivotal for improved dental diagnostics, offering precise identification of dental anomalies. This could revolutionize clinical dental settings by facilitating more accurate treatments and safer examination processes with lower radiation exposure, thus enhancing overall patient care.