1 research outputs found
Automated Segmentation of Lesions in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets
Breast cancer is the most common invasive cancer with the highest cancer
occurrence in females. Handheld ultrasound is one of the most efficient ways to
identify and diagnose the breast cancer. The area and the shape information of
a lesion is very helpful for clinicians to make diagnostic decisions. In this
study we propose a new deep-learning scheme, semi-pixel-wise cycle generative
adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method
takes the advantage of a fully connected convolutional neural network (FCN) and
a generative adversarial net to segment a lesion by using prior knowledge. We
compared the proposed method to a fully connected neural network and the level
set segmentation method on a test dataset consisting of 32 malignant lesions
and 109 benign lesions. Our proposed method achieved a Dice similarity
coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79
respectively. Particularly, for malignant lesions, our method increases the DSC
(0.90) of the fully connected neural network to 0.93 significantly (p0.001).
The results show that our SPCGAN can obtain robust segmentation results and may
be used to relieve the radiologists' burden for annotation