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Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions.

Zhiyang Liu^1,2, Dong Yang¹, Minghao Zhang¹

¹College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.

Bioengineering (Basel, Switzerland)

|April 27, 2024

Summary

This study introduces FAUNet, an improved AI model for segmenting the inferior alveolar nerve (IAN) canal in dental scans. FAUNet enhances accuracy in identifying the IAN canal, crucial for preventing nerve damage during dental procedures.

Keywords:

attention mechanism convolutional neural network frequency-domain attention inferior alveolar nerve medical image segmentation

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Accurate segmentation of the inferior alveolar nerve (IAN) canal is critical in dental procedures to prevent nerve injury.
Detecting the IAN canal in dental cone beam computed tomography (CBCT) is challenging due to its thin, small, and multi-slice nature.
Current segmentation methods struggle with the precise identification of IAN canals.

Purpose of the Study:

To enhance the accuracy of inferior alveolar nerve (IAN) canal segmentation using a novel deep learning approach.
To introduce and evaluate the performance of a frequency-domain attention mechanism integrated into the UNet architecture.
To compare the proposed method against classical UNet and other competitive techniques.

Main Methods:

Development of a Frequency Attention UNet (FAUNet) by integrating a frequency-domain attention mechanism into the UNet architecture.
Evaluation of FAUNet's segmentation performance using Dice and surface Dice coefficients on dental CBCT data.
Comparison of FAUNet's performance and parameter efficiency against the classical UNet and other state-of-the-art methods.

Main Results:

FAUNet achieved Dice coefficients of 75.55% and surface Dice coefficients of 81.35%.
The proposed FAUNet demonstrated significant improvement over the classical UNet, with a 2.39% gain in Dice coefficient and a 2.82% gain in surface Dice coefficient.
The integration of only 224 additional parameters resulted in substantial performance enhancements.

Conclusions:

The proposed FAUNet significantly improves the accuracy of inferior alveolar nerve (IAN) canal segmentation compared to existing methods.
Frequency-domain attention mechanisms offer a promising advantage over spatial-domain counterparts for medical image segmentation tasks.
FAUNet presents a valuable tool for improving safety and precision in dental procedures involving the IAN canal.