Tympanic membrane segmentation of video frames to create composite images using SAM

Seda Camalan; Muhammad Khalid Khan Niazi; Charles Elmaraghy; Aaron C. Moberly; Metin N. Gurcan

doi:10.1117/12.3006926

3 April 2024 Tympanic membrane segmentation of video frames to create composite images using SAM

Seda Camalan, Muhammad Khalid Khan Niazi, Charles Elmaraghy, Aaron C. Moberly, Metin N. Gurcan

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292736 (2024) https://doi.org/10.1117/12.3006926
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Seda Camalan, Muhammad Khalid Khan Niazi, Charles Elmaraghy, Aaron C. Moberly, and Metin N. Gurcan "Tympanic membrane segmentation of video frames to create composite images using SAM", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292736 (3 April 2024); https://doi.org/10.1117/12.3006926

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