
DBF-Net is a novel deep learning architecture that improves the accuracy of ultrasound image segmentation, particularly at lesion boundaries, by fusing information from both body and boundary features.


coremsg

dbf-net-a-dual-branch-network-with-feature-fusion-for-enhanced-ultrasound-image-segmentation-by-leveraging-body-and-boundary-information


DBF-Net: A Dual-Branch Network with Feature Fusion for Enhanced Ultrasound Image Segmentation by Leveraging Body and Boundary Information


title_rewrite


This paper introduces BRAU-Net, a novel deep learning architecture that leverages a dynamic sparse attention mechanism and an inverted bottleneck patch expanding module to achieve state-of-the-art results in pubic symphysis-fetal head segmentation on transperineal ultrasound images.


brau-net-a-novel-deep-learning-approach-for-pubic-symphysis-fetal-head-segmentation-in-transperineal-ultrasound-images


BRAU-Net: A Novel Deep Learning Approach for Pubic Symphysis-Fetal Head Segmentation in Transperineal Ultrasound Images
