Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network
Computer methods and programs in biomedicine, 2020•Elsevier
Background and objective Automatic segmentation of breast lesion from ultrasound images
is a crucial module for the computer aided diagnostic systems in clinical practice. Large-
scale breast ultrasound (BUS) images remain unannotated and need to be effectively
explored to improve the segmentation quality. To address this, a semi-supervised
segmentation network is proposed based on generative adversarial networks (GAN).
Methods In this paper, a semi-supervised learning model, denoted as BUS-GAN, consisting …
is a crucial module for the computer aided diagnostic systems in clinical practice. Large-
scale breast ultrasound (BUS) images remain unannotated and need to be effectively
explored to improve the segmentation quality. To address this, a semi-supervised
segmentation network is proposed based on generative adversarial networks (GAN).
Methods In this paper, a semi-supervised learning model, denoted as BUS-GAN, consisting …
Background and objective
Automatic segmentation of breast lesion from ultrasound images is a crucial module for the computer aided diagnostic systems in clinical practice. Large-scale breast ultrasound (BUS) images remain unannotated and need to be effectively explored to improve the segmentation quality. To address this, a semi-supervised segmentation network is proposed based on generative adversarial networks (GAN).
Methods
In this paper, a semi-supervised learning model, denoted as BUS-GAN, consisting of a segmentation base network—BUS-S and an evaluation base network—BUS-E, is proposed. The BUS-S network can densely extract multi-scale features in order to accommodate the individual variance of breast lesion, thereby enhancing the robustness of segmentation. Besides, the BUS-E network adopts a dual-attentive-fusion block having two independent spatial attention paths on the predicted segmentation map and leverages the corresponding original image to distill geometrical-level and intensity-level information, respectively, so that to enlarge the difference between lesion region and background, thus improving the discriminative ability of the BUS-E network. Then, through adversarial training, the BUS-GAN model can achieve higher segmentation quality because the BUS-E network guides the BUS-S network to generate more accurate segmentation maps with more similar distribution as ground truth.
Results
The counterpart semi-supervised segmentation methods and the proposed BUS-GAN model were trained with 2000 in-house images, including 100 annotated images and 1900 unannotated images, and tested on two different sites, including 800 in-house images and 163 public images. The results validate that the proposed BUS-GAN model can achieve higher segmentation accuracy on both the in-house testing dataset and the public dataset than state-of-the-art semi-supervised segmentation methods.
Conclusions
The developed BUS-GAN model can effectively utilize the unannotated breast ultrasound images to improve the segmentation quality. In the future, the proposed segmentation method can be a potential module for the automatic breast ultrasound diagnose system, thus relieving the burden of a tedious image annotation process and alleviating the subjective influence of physicians’ experiences in clinical practice. Our code will be made available on https://github.com/fiy2W/BUS-GAN.
Elsevier