轻量级网络精准分割前纵隔，助力罕见病自动检测
A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation
- 基于U型结构与双注意力机制，提升长程依赖建模能力
- 在2775例数据上达87.83% Dice系数，优于主流分割模型
- 适合医疗影像中低频病灶的自动化分析，适用于放射科辅助
为实现前纵隔病变（AML）的自动检测，需针对前纵隔（AM）设计专用自动分割模型。由于AML发病率极低，类似肺癌筛查的研究难以开展，回顾性分析胸部CT以评估其患病率耗时巨大。因此，开发人工智能模型定位前纵隔，有助于提升放射科医生的工作效率与诊断准确率。本文提出一种U型结构网络，引入两种注意力机制以保持长距离依赖和精确定位。为兼具多头自注意力（MHSA）能力与轻量化设计，提出并行式多头自注意力模块W-MHSA；为在上采样过程中维持长程依赖，设计扩张深度可分离并行路径（DDWPP）。为实现轻量化架构，引入扩展卷积块，并与W-MHSA结合用于编码器特征提取。模型在2775例前纵隔病例上训练，平均Dice相似系数达87.83%，平均交并比（IoU）为79.16%，灵敏度为89.60%。相比Trans Unet、Attention Unet、Res Unet及Res Unet++等先进网络，本方法表现更优。
原文摘要 · Abstract (English)
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++.
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