A novel CNN+LSTM approach to classify frontal chest x-rays for spine fractures

Larissa C. Schudlo; Yiting Xie; Kirstin Small; Ben Graf

doi:10.1117/12.2607416

4 April 2022 A novel CNN+LSTM approach to classify frontal chest x-rays for spine fractures

Larissa C. Schudlo, Yiting Xie, Kirstin Small, Ben Graf

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331B (2022) https://doi.org/10.1117/12.2607416
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Spine fractures are serious injuries that are often missed on standard chest x-rays, especially the frontal view. Automatic detection using artificial intelligence could help reduce these missed findings. To detect fractures, radiologists compare the morphological characteristics within the ordered vertebrae sequence. To this end, we designed a time-distributed CNN+LSTM model with Res-Net-inspired backbone to classify a sequence of vertebral image patches extracted from a frontal chest x-ray. While the CNN component encapsulates spatial characteristics of vertebrae, the time-distributed aspect ensures the sequential information of each image is explicitly preserved and better exploited by the LSTM. A Dense UNet was trained to automatically detect vertebrae locations for patch extraction. For this retrospective researchonly study, a comparative analysis was performed using six random partitions of 1250 images, and the AUCs for each partition were averaged. Using manual vertebrae detection to extract vertebral images patches from the x-rays, the proposed method achieved an average AUC of 83.17 ± 3.8%. Using the Dense UNet for automatic vertebrae detection in the pipeline, the proposed method achieved an average AUC of 79.3 ± 1.8% in differentiating x-rays with and without vertebral fractures. This is an average improvement of 6.0% relative to the same CNN+LSTM architecture that is not time distributed. Using a model ensemble, an AUC of 82.7 ± 3.7% was achieved. These findings suggest the importance of exploiting both sequential and spatial information for fracture detection.

Conference Presentation

Citation Download Citation

Larissa C. Schudlo, Yiting Xie, Kirstin Small, and Ben Graf "A novel CNN+LSTM approach to classify frontal chest x-rays for spine fractures", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331B (4 April 2022); https://doi.org/10.1117/12.2607416

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

Includes PDF, HTML & Video, when available