Classification of point-of-care ultrasound in breast imaging using deep learning

Jennie Karlsson; Ida Arvidsson; Freja Sahlin; Kalle Åström; Niels Christian Overgaard; Kristina Lång; Anders Heyden

doi:10.1117/12.2654251

7 April 2023 Classification of point-of-care ultrasound in breast imaging using deep learning

Jennie Karlsson, Ida Arvidsson, Freja Sahlin, Kalle Åström, Niels Christian Overgaard, Kristina Lång, Anders Heyden

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650Y (2023) https://doi.org/10.1117/12.2654251
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Early detection of breast cancer is important to reduce morbidity and mortality. Access to breast imaging is limited in low- and middle-income countries compared to high-income countries. This contributes to advancestage breast cancer presentation with poor survival. Pocket-sized portable ultrasound device, also known as point-of-care ultrasound (POCUS), aided by decision support using deep learning-based algorithms for lesion classification could be a cost-effective way to enable access to breast imaging in low-resource settings. A previous study, where using convolutional neural networks (CNN) to classify breast cancer in conventional ultrasound (US) images, showed promising results. The aim of the present study is to classify POCUS breast images. A POCUS data set containing 1100 breast images was collected. To increase the size of the data set, a CycleConsistent Adversarial Network (CycleGAN) was trained on US images to generate synthetic POCUS images. A CNN was implemented, trained, validated and tested on POCUS images. To improve performance, the CNN was trained with different combinations of data consisting of POCUS images, US images, CycleGAN-generated POCUS images and spatial augmentation. The best result was achieved by a CNN trained on a combination of POCUS images and CycleGAN-generated POCUS images and augmentation. This combination achieved a 95% confidence interval for AUC between 93.5% – 96.6%.

Conference Presentation

Citation Download Citation

Jennie Karlsson, Ida Arvidsson, Freja Sahlin, Kalle Åström, Niels Christian Overgaard, Kristina Lång, and Anders Heyden "Classification of point-of-care ultrasound in breast imaging using deep learning", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650Y (7 April 2023); https://doi.org/10.1117/12.2654251

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