标题： 基于U形结构和注意力机制的轻量级卷积神经网络用于前纵隔分割 显示英文标题

标题： A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation

摘要： 为了自动检测前纵隔病变（AMLs），首要需求是一种专门设计用于前纵隔（AM）的自动分割模型。AML 的患病率极低，这使得开展类似于肺癌筛查的研究变得极具挑战性。回顾性审查特定时间段内的胸部CT扫描以调查AML的患病率需要大量时间。因此，开发一种人工智能（AI）模型来定位前纵隔有助于放射科医生提高其处理工作量的能力，并提升AML的诊断准确性。本文介绍了一种U形结构网络用于分割前纵隔。为了保持长距离依赖性和定位，我们使用了两种注意力机制。为了具有多头自注意力（MHSA）和轻量级网络的潜力，我们设计了一个名为宽多头自注意力（W-MHSA）的并行MHSA。当上采样特征图时，保持长距离依赖关系对于分割至关重要。因此，我们为此目的设计了一种膨胀深度平行路径连接（DDWPP）。为了设计一个轻量级架构，我们在所提出的U形网络的编码器部分引入了一个扩展卷积块，并将其与提出的W-MHSA结合，用于特征提取。所提出的网络在2775个前纵隔病例上进行了训练，获得了平均Dice相似系数（DSC）为87.83%，平均交并比（IoU）为79.16%，敏感度为89.60%。与最先进的分割网络（如Trans Unet、Attention Unet、Res Unet和Res Unet++）相比，我们提出的架构表现出优越的分割性能。 显示英文摘要

摘要： To automatically detect Anterior Mediastinum Lesions (AMLs) in the Anterior Mediastinum (AM), the primary requirement will be an automatic segmentation model specifically designed for the AM. The prevalence of AML is extremely low, making it challenging to conduct screening research similar to lung cancer screening. Retrospectively reviewing chest CT scans over a specific period to investigate the prevalence of AML requires substantial time. Therefore, developing an Artificial Intelligence (AI) model to find location of AM helps radiologist to enhance their ability to manage workloads and improve diagnostic accuracy for AMLs. In this paper, we introduce a U-shaped structure network to segment AM. Two attention mechanisms were used for maintaining long-range dependencies and localization. In order to have the potential of Multi-Head Self-Attention (MHSA) and a lightweight network, we designed a parallel MHSA named Wide-MHSA (W-MHSA). Maintaining long-range dependencies is crucial for segmentation when we upsample feature maps. Therefore, we designed a Dilated Depth-Wise Parallel Path connection (DDWPP) for this purpose. In order to design a lightweight architecture, we introduced an expanding convolution block and combine it with the proposed W-MHSA for feature extraction in the encoder part of the proposed U-shaped network. The proposed network was trained on 2775 AM cases, which obtained an average Dice Similarity Coefficient (DSC) of 87.83%, mean Intersection over Union (IoU) of 79.16%, and Sensitivity of 89.60%. Our proposed architecture exhibited superior segmentation performance compared to the most advanced segmentation networks, such as Trans Unet, Attention Unet, Res Unet, and Res Unet++.