Automatic segmentation and measurement of tongue geometric features using TOSA-Net for obstructive sleep apnoea.

PURPOSE: The tongue is a critical soft tissue of the upper airway; excessive enlargement can narrow or collapse the airway, contributing to obstructive sleep apnea (OSA). To our knowledge, no existing methods quantify OSA-related tongue geometric features using deep learning segmentation. We present a deep learning model (TOSA-Net) capable of accurately segmenting and measuring tongue geometry, offering a more efficient approach for future OSA tongue research.

MATERIALS AND

METHODS

A dataset (n = 207) of front and profile tongue images, along with manually segmented and quantified tongue dimensions, was used for model development and evaluation. We modified a U-Net architecture using multi-scale convolution filters for feature extraction. Automated segmentations were used to calculate tongue geometric features. Dice coefficient, Pearson correlation, agreement analyses, and expert-derived clinical parameters assessed segmentation and measurement accuracy between deep learning and manual methods.

RESULTS

Five-fold cross-validation yielded a mean Dice coefficient of 0.870 across all subjects, with the highest mean Dice (0.914) in OSA patients. Tongue features (area, length, thickness, curvature) showed strong correlation with manual measurements, with no statistically significant differences [Formula: see text]).

CONCLUSION

The high accuracy of automated segmentation and measurement indicates the proposed method's potential to replace time-consuming manual tasks in clinical and large-scale OSA research.