现代电子技术2024，Vol.47Issue(15)：65-72,8.DOI:10.16652/j.issn.1004-373x.2024.15.011
改进Segformer的前列腺超声图像语义分割算法
Prostate ultrasound image semantic segmentation algorithm based on improved Segformer
摘要
Abstract
Accurate segmentation of prostate ultrasound images in clinical settings plays a significant role in subsequent diagnosis.Therefore,it is essential to rapidly and accurately segment the prostate boundary with deep learning assistance.To this end,a novel prostate segmentation network named DA-Segformer is proposed.In this network,the Transformer,deep supervision and attention mechanism are utilized to segment prostate ultrasound images rapidly and accurately.Additionally,the MAG module is introduced to enhance the network's understanding of feature maps and pixel correlations,so as to improve its sensitivity to foreground pixels.A deep supervision strategy is employed.A loss function is introduced into the decoding process to optimize gradient propagation,so as to enhance the network's ability to learn and represent the key features.Experimental results demonstrate that the mIoU(mean intersection over union),Dice coefficient,accuracy rate and recall rate of the DA-Segformer model on the prostate ultrasound image dataset are superior to those of the other mainstream semantic segmentation models.The proposed method effectively addresses the challenge of manual segmentation of prostate ultrasound images,and provides valuable computer-aided tools for clinical diagnosis.关键词
医学图像分割/超声图像分割/Transformer/门控注意力/深监督/扩张卷积/梯度下降/多尺度特征Key words
medical image segmentation/ultrasound image segmentation/Transformer/gated attention/deep supervision/dilated convolution/gradient descent/multi-scale feature分类
信息技术与安全科学引用本文复制引用
石勇涛,柳迪,高超,杜威,邱康齐..改进Segformer的前列腺超声图像语义分割算法[J].现代电子技术,2024,47(15):65-72,8.基金项目
国家自然科学基金资助项目(61871258):面向大坝变形监测的视频微小运动放大与四维视觉增强 （61871258）
湖北省中央引导地方科技发展专项(2019ZYYD007) （2019ZYYD007）
