4.4 Article

The dilated dense U-net for spinal fracture lesions segmentation

期刊

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 41, 期 1, 页码 2291-2304

出版社

IOS PRESS
DOI: 10.3233/JIFS-211063

关键词

Deep learning; Segmentation; Dense U-net; DDU-net(Dilated Dense U-net)

资金

  1. [61172147]
  2. [61502365]

向作者/读者索取更多资源

This paper introduces a method to objectively segment spinal lesions by combining U-net, dense blocks, and dilated convolution, providing assistance in diagnosing spinal diseases and serving as a clinical reference. The proposed DDU-net shows good segmentation performance of spinal lesions, laying a solid foundation for both doctors and patients.
With the development of computer technology, more and more deep learning algorithms are widely used in medical image processing. Viewing CT images is a very usual and important way in diagnosing spinal fracture diseases, but correctly reading CT images and effectively segmenting spinal lesions or not is deeply depended on doctors' clinical experiences. In this paper, we present a method of combining U-net, dense blocks and dilated convolution to segment lesions objectively, so as to give a help in diagnosing spinal diseases and provide a reference clinically. First, we preprocess and augment CT images of spinal lesions. Second, we present the DenseU-net network model consists of dense blocks and U-net to raise the depth of training network. Third, we introduce dilated convolution into DenseU-net to construct proposed DDU-net(Dilated Dense U-net), in order to raise receptive field of CT images for getting more lesions information. The experiments show that DDU-net has a good segmentation performance of spinal lesions, which can build a solid foundation for both doctors and patients.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据