Breast mass is one of the most distinctive signs for the diagnosis of breast cancer, and the accurate segmentation of masses is critical for improving the accuracy of breast cancer detection and reducing the mortality rate. It is time-consuming for a physician to review the film. Besides, traditional medical segmentation techniques often require prior knowledge or manual extraction of features, which often lead to a subjective diagnosis. Therefore, developing an automatic image segmentation method is important for clinical application. In this paper, a fully automatic method based on deep learning for breast mass segmentation is proposed, which combines densely connected U-Net with attention gates (AGs). It contains an encoder and a decoder. The encoder is a densely connected convolutional network and the decoder is the decoder of U-Net integrated with AGs. The proposed method is tested on the public and authoritative database-Digital Database for Screening Mammography (DDSM) database. F1-score, mean intersection over union, sensitivity, specificity, and overall accuracy are used to evaluate the effectiveness of the proposed method. The experimental results show that dense U-Net integrated AGs achieve better segmentation results than U-Net, attention U-Net, DenseNet, and state-of-the-art methods. INDEX TERMS Breast masses segmentation, deep learning, biomedical image processing, attention gates, densely connected convolutional network.
Messenger RNA 3′-end formation is an essential posttranscriptional processing step for most eukaryotic genes. Different from plants and animals where AAUAAA and its variants routinely are found as the main poly(A) signal, Chlamydomonas reinhardtii uses UGUAA as the major poly(A) signal. The advance of sequencing technology provides an enormous amount of sequencing data for us to explore the variations of poly(A) signals, alternative polyadenylation (APA), and its relationship with splicing in this algal species. Through genome-wide analysis of poly(A) sites in C. reinhardtii, we identified a large number of poly(A) sites: 21,041 from Sanger expressed sequence tags, 88,184 from 454, and 195,266 from Illumina sequence reads. In comparison with previous collections, more new poly(A) sites are found in coding sequences and intron and intergenic regions by deep-sequencing. Interestingly, G-rich signals are particularly abundant in intron and intergenic regions. The prevalence of different poly(A) signals between coding sequences and a 3′-untranslated region implies potentially different polyadenylation mechanisms. Our data suggest that the APA occurs in about 68% of C. reinhardtii genes. Using Gene Ontolgy analysis, we found most of the APA genes are involved in RNA regulation and metabolic process, protein synthesis, hydrolase, and ligase activities. Moreover, intronic poly(A) sites are more abundant in constitutively spliced introns than retained introns, suggesting an interplay between polyadenylation and splicing. Our results support that APA, as in higher eukaryotes, may play significant roles in increasing transcriptome diversity and gene expression regulation in this algal species. Our datasets also provide useful information for accurate annotation of transcript ends in C. reinhardtii.
Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.
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