Adaptive loss engine for x-ray segmentation (ALEXS) for scoliosis intervention: assess digital segmentation and angle approximation

Yunbo Shao; Shuo Li

doi:10.1117/12.3008795

29 March 2024 Adaptive loss engine for x-ray segmentation (ALEXS) for scoliosis intervention: assess digital segmentation and angle approximation

Yunbo Shao, Shuo Li

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 1292812 (2024) https://doi.org/10.1117/12.3008795
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Estimating the severity of scoliosis is time consuming and imprecise. Doctors currently manually identify each vertebra and measure the Cobb angles, a measurement used for scoliosis. This paper aims to contribute to developing a fully automated method of estimating Cobb angles. Historically, the greatest challenge of this goal has been identifying the location of vertebrae within the x-ray image This paper proposes the use of an image segmentation model for this purpose, since they can identify objects within images with high accuracy. However, their results are less accurate when given xrays. To solve this issue, a new specialized model was trained on additional data composed entirely of x-rays, and it was named Adaptive Loss Engine for X-Ray Segmentation (ALEXS). ALEXS is a self-improving model capable of automatically identifying vertebrae, providing noticeable improved segmentation results compared to models that have not been trained on x-rays. One method of helping the model identify more vertebrae is altering the original x-ray image without changing the locations of the vertebrae. It was found that among many image processing techniques, sharpening the image and increasing its contrast had the largest positive effect on the results, allowing ALEXS to identify many more vertebrae than before. Based on the results that were obtained, using ALEXS combined with altered images produces superior results compared to some previous attempts, with improvements in the accuracy of the produced segments. These improved methods allow for a more accurate end-to-end process for automatically diagnosing scoliosis.

Conference Presentation

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

Citation Download Citation

Yunbo Shao and Shuo Li "Adaptive loss engine for x-ray segmentation (ALEXS) for scoliosis intervention: assess digital segmentation and angle approximation", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 1292812 (29 March 2024); https://doi.org/10.1117/12.3008795

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE