FA-Net：基于层次特征融合与交互注意机制的肝癌患者剂量预测网络

FA-Net: A hierarchical feature fusion and interactive attention-based network for dose prediction in liver cancer patients

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ARTIFICIAL INTELLIGENCE IN MEDICINE

影响因子:6.2

分区:医学2区 Top / 计算机：人工智能2区工程：生物医学2区医学：信息2区

发表日期:2024 Oct

作者: Miao Liao, Shuanhu Di, Yuqian Zhao, Wei Liang, Zhen Yang

DOI: 10.1016/j.artmed.2024.102961

keywords: Deep learning network; Dose prediction; Liver cancer; Radiation therapy

摘要

剂量预测是肝癌放射治疗自动化计划中的关键步骤。已有多种深度学习方法用于剂量预测，以提升放疗方案的设计效率和质量。然而，这些方法通常将CT图像、危及器官（OAR）轮廓及计划靶体积（PTV）作为多通道输入，难以从每个输入中提取充分的特征信息，导致剂量分布效果不理想。为此，本文提出一种基于层次特征融合与交互注意的肝癌剂量预测新网络。首先构建特征提取模块，从不同输入中提取多尺度特征；然后设计层次特征融合模块，将多尺度特征进行逐层融合；最后，采用基于注意力机制的解码器，将融合后的特征逐步重建为剂量分布。此外，还设计了自编码器网络，在训练阶段引入感知损失，以提升预测精度。该方法在私有临床数据集上进行测试，得到HI和CI分别为0.31和0.87，优于几种现有方法。实验结果表明，所提方法生成的剂量分布接近临床批准水平。代码开源地址为https://github.com/hired-ld/FA-Net。

Abstract

Dose prediction is a crucial step in automated radiotherapy planning for liver cancer. Several deep learning-based approaches for dose prediction have been proposed to enhance the design efficiency and quality of radiotherapy plan. However, these approaches usually take CT images and contours of organs at risk (OARs) and planning target volume (PTV) as a multi-channel input and is thus difficult to extract sufficient feature information from each input, which results in unsatisfactory dose distribution. In this paper, we propose a novel dose prediction network for liver cancer based on hierarchical feature fusion and interactive attention. A feature extraction module is first constructed to extract multi-scale features from different inputs, and a hierarchical feature fusion module is then built to fuse these multi-scale features hierarchically. A decoder based on attention mechanism is designed to gradually reconstruct the fused features into dose distribution. Additionally, we design an autoencoder network to generate a perceptual loss during training stage, which is used to improve the accuracy of dose prediction. The proposed method is tested on private clinical dataset and obtains HI and CI of 0.31 and 0.87, respectively. The experimental results are better than those by several existing methods, indicating that the dose distribution generated by the proposed method is close to that approved in clinics. The codes are available at https://github.com/hired-ld/FA-Net.