TBE-Net: A Deep Network Based on Tree-like Branch Encoder for Medical Image Segmentation.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shukai Yang, Xiaoqian Zhang, Youdong He, Yufeng Chen, Ying Zhou
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引用次数: 0

Abstract

In recent years, encoder-decoder-based network structures have been widely used in designing medical image segmentation models. However, these methods still face some limitations: 1) The network's feature extraction capability is limited, primarily due to insufficient attention to the encoder, resulting in a failure to extract rich and effective features. 2) Unidirectional stepwise decoding of smaller-sized feature maps restricts segmentation performance. To address the above limitations, we propose an innovative Tree-like Branch Encoder Network (TBE-Net), which adopts a tree-like branch encoder to better perform feature extraction and preserve feature information. Additionally, we introduce the Depth and Width Expansion (D-WE) module to expand the network depth and width at low parameter cost, thereby enhancing network performance. Furthermore, we design a Deep Aggregation Module (DAM) to better aggregate and process encoder features. Subsequently, we directly decode the aggregated features to generate the segmentation map. The experimental results show that, compared to other advanced algorithms, our method, with the lowest parameter cost, achieved improvements in the IoU metric on the TNBC, PH2, CHASE-DB1, STARE, and COVID-19-CT-Seg datasets by 1.6%, 0.46%, 0.81%, 1.96%, and 0.86%, respectively.

TBE-Net:基于树状分支编码器的深度网络,用于医学图像分割
近年来,基于编码器-解码器的网络结构被广泛用于设计医学图像分割模型。然而,这些方法仍面临一些局限性:1) 网络的特征提取能力有限,主要原因是对编码器的关注不够,导致无法提取丰富有效的特征。2) 对较小尺寸的特征图进行单向逐步解码限制了分割性能。针对上述局限,我们提出了一种创新的树状分支编码器网络(TBE-Net),它采用树状分支编码器,能更好地进行特征提取并保留特征信息。此外,我们还引入了深度和宽度扩展(D-WE)模块,以较低的参数成本扩展网络深度和宽度,从而提高网络性能。此外,我们还设计了深度聚合模块(DAM),以更好地聚合和处理编码器特征。随后,我们直接对聚合特征进行解码,生成分割图。实验结果表明,与其他先进算法相比,我们的方法参数成本最低,在 TNBC、PH2、CHASE-DB1、STARE 和 COVID-19-CT-Seg 数据集上的 IoU 指标分别提高了 1.6%、0.46%、0.81%、1.96% 和 0.86%。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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