Hearing loss as a significant global health concern, encompassing costs across society through healthcare, education, and productivity impacts. Traditional otoscopic diagnostic methods pose challenges, prompting the development of computer-aided diagnosis (CAD) systems. Tympanic membrane (TM) segmentation is a crucial and vital task for early diagnosis and intervention in middle ear diseases. Automatic TM segmentation in CAD systems improves diagnostic accuracy. This study presents a method for the automatic segmentation of the TM from video-otoscopic frames based on Segment Anything Model Adapter (SAM-Adapter). To the best of our knowledge, this research is the first application of a SAM-Adapter segmentation model for segmenting TM areas from otoscopic frames. 765 video frames from 36 otoscopic videos were used to train and test the model. The experimental results show that the SAM-Adapter achieves high segmentation performance with a Dice similarity coefficient of 0.9486 without any pre-processing and postprocessing steps. Empirical results showed that the SAM-Adapter model is better than the U-Net-based models in our dataset.
|