2 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
CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation
Breast ultrasound (BUS) image segmentation plays a crucial role in a
computer-aided diagnosis system, which is regarded as a useful tool to help
increase the accuracy of breast cancer diagnosis. Recently, many deep learning
methods have been developed for segmentation of BUS image and show some
advantages compared with conventional region-, model-, and traditional
learning-based methods. However, previous deep learning methods typically use
skip-connection to concatenate the encoder and decoder, which might not make
full fusion of coarse-to-fine features from encoder and decoder. Since the
structure and edge of lesion in BUS image are common blurred, these would make
it difficult to learn the discriminant information of structure and edge, and
reduce the performance. To this end, we propose and evaluate a coarse-to-fine
fusion convolutional network (CF2-Net) based on a novel feature integration
strategy (forming an 'E'-like type) for BUS image segmentation. To enhance
contour and provide structural information, we concatenate a super-pixel image
and the original image as the input of CF2-Net. Meanwhile, to highlight the
differences in the lesion regions with variable sizes and relieve the imbalance
issue, we further design a weighted-balanced loss function to train the CF2-Net
effectively. The proposed CF2-Net was evaluated on an open dataset by using
four-fold cross validation. The results of the experiment demonstrate that the
CF2-Net obtains state-of-the-art performance when compared with other deep
learning-based methodsComment: 8 pages, 6 figure