Breast ultrasound lesions recognition: end-to-end deep learning approaches

Moi Hoon Yap; Manu Goyal; Fatima M. Osman; Robert Martí; Erika Denton; Arne Juette; Reyer Zwiggelaar

doi:10.1117/1.JMI.6.1.011007

10 October 2018 Breast ultrasound lesions recognition: end-to-end deep learning approaches

Moi Hoon Yap, Manu Goyal, Fatima M. Osman, Robert Martí, Erika Denton, Arne Juette, Reyer Zwiggelaar

Author Affiliations +

Journal of Medical Imaging, Vol. 6, Issue 1, 011007 (October 2018). https://doi.org/10.1117/1.JMI.6.1.011007

Abstract

Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. To assess the performance, we conduct fivefold cross validation using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results showed that our proposed method performed better on benign lesions, with a top “mean Dice” score of 0.7626 with FCN-16s, when compared with the malignant lesions with a top mean Dice score of 0.5484 with FCN-8s. When considering the number of images with Dice score >0.5, 89.6% of the benign lesions were successfully segmented and correctly recognised, whereas 60.6% of the malignant lesions were successfully segmented and correctly recognized. We conclude the paper by addressing the future challenges of the work.

Citation Download Citation

Moi Hoon Yap, Manu Goyal, Fatima M. Osman, Robert Martí, Erika Denton, Arne Juette, and Reyer Zwiggelaar "Breast ultrasound lesions recognition: end-to-end deep learning approaches," Journal of Medical Imaging 6(1), 011007 (10 October 2018). https://doi.org/10.1117/1.JMI.6.1.011007

Received: 4 June 2018; Accepted: 20 August 2018; Published: 10 October 2018

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