Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in their lifetime. Mortality due to breast cancer can be reduced by its early and precise diagnosis. Many studies have investigated methods for segmentation, and computer-aided diagnosis based on deep learning techniques, in particular, has recently gained attention. However, recently proposed methods such as fully convolutional network (FCN), SegNet, and U-Net still need to be further improved to provide better semantic segmentation when diagnosing breast cancer by ultrasound imaging, because of their low performance. In this article, we propose a channel attention module with multiscale grid average pooling (MSGRAP) for the precise segmentation of breast cancer regions in ultrasound images. We demonstrate the effectiveness of the channel attention module with MSGRAP for semantic segmentation and develop a novel semantic segmentation network with the proposed attention module for the precise segmentation of breast cancer regions in ultrasound images. While a conventional convolutional operation cannot use global spatial information on input images and only use the small local information in a kernel of a convolution filter, the proposed attention module allows using both global and local spatial information. In addition, through ablation studies, we come up with a network architecture for precise breast cancer segmentation in an ultrasound image. The proposed network was constructed with an open-source breast cancer ultrasound image data set, and its performance was compared with those of other state-of-the-art deep-learning models for the segmentation of breast cancer. The experimental results showed that our network outperformed other segmentation methods, and the proposed channel attention module improved the performance of the network for breast cancer segmentation in ultrasound images.

Channel Attention Module With Multiscale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image

A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound

Self-co-attention neural network for anatomy segmentation in whole breast ultrasound

Segmentation of the breast ultrasound (BUS) image is an important step for subsequent assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have achieved satisfactory performance in many computer vision tasks, especially in medical image segmentation. Nevertheless, those methods always require a large number of pixel-wise labeled data that is expensive in medical practices. In this study, we propose a new segmentation method by dense prediction and local fusion of superpixels for breast anatomy with scarce labeled data. First, the proposed method generates superpixels from the BUS image enhanced by histogram equalization, a bilateral filter, and a pyramid mean shift filter. Second, using a convolutional neural network (CNN) and distance metric learning-based classifier, the superpixels are projected onto the embedding space and then classified by calculating the distance between superpixels’ embeddings and the centers of categories. By using superpixels, we can generate a large number of training samples from each BUS image. Therefore, the problem of the scarcity of labeled data can be better solved. To avoid the misclassification of the superpixels, $K$ -nearest neighbor (KNN) is used to reclassify the superpixels within every local region based on the spatial relationships among them. Fivefold cross-validation was taken and the experimental results show that our method outperforms several often used deep-learning methods under the condition of the absence of a large number of labeled data (48 BUS images for training and 12 BUS images for testing).

Dense Prediction and Local Fusion of Superpixels: A Framework for Breast Anatomy Segmentation in Ultrasound Image With Scarce Data

A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning

Segmentation of breast ultrasound image with semantic classification of superpixels.

OBJECTIVES
Although the guidelines clearly recommend the fasting time of children before anesthesia, it is usually difficult to control. For pediatric patients, prolonged fasting time before surgery will lead to dehydration and hypoglycemia. Adenotonsillectomy is one of the most common operations in pediatric patients, but its complications are not rare. The purpose of this study is to analyze the relationship between preoperative fasting time and postoperative complications in children undergoing adenotonsillectomy.


METHODS
The medical and surgical records of 480 pediatric patients who underwent adenotonsillectomy were analyzed retrospectively. They were divided into three groups, including adenoidectomy group, tonsillectomy group, and adenotonsillectomy group. Logistic regression analysis was used to analyze the effect of preoperative fasting time on postoperative complications and hospital stay in pediatric patients of the three groups.


RESULTS
The postoperative bleeding rate in the adenoidectomy group (5.16%) was lower than tonsillectomy group and adenoidectomy group (P < .001). Logistic regression analysis showed that the fasting time was positively correlated with the vomiting and pain in adenoidectomy group, tonsillectomy group, and adenotonsillectomy group. And, the postoperative hospital stay was also positively correlated with fasting time in three groups.


CONCLUSION
The prolonged fasting time before otolaryngology surgery in children is related to the occurrence of postoperative complications like vomiting and pain, and also to the increase of postoperative hospital stay.

Yonghao Huang

Papers

Segmentation of breast ultrasound image with semantic classification of superpixels.

Effect of fasting time before anesthesia on postoperative complications in children undergoing adenotonsillectomy.