FA-NET：一种分层特征融合和基于互动注意力的网络，用于肝癌患者的剂量预测
FA-Net: A hierarchical feature fusion and interactive attention-based network for dose prediction in liver cancer patients
影响因子:6.20000
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
摘要
剂量预测是肝癌自动放疗计划的关键步骤。已经提出了几种基于学习的剂量预测方法，以提高放射治疗计划的设计效率和质量。但是，这些方法通常以风险（OARS）和计划目标体积（PTV）作为多通道输入的CT图像和轮廓作为多通道输入，因此很难从每个输入中提取足够的特征信息，从而导致剂量分布不满意。在本文中，我们提出了一个基于层次特征融合和互动注意力的肝癌的新型剂量预测网络。首先构建了一个功能提取模块，以从不同输入中提取多尺度特征，然后构建一个分层特征融合模块，以融合这些多尺度功能。基于注意机制的解码器旨在逐渐将融合特征重建为剂量分布。此外，我们设计了一个自动编码器网络，以在训练阶段产生感知损失，该网络用于提高剂量预测的准确性。提出的方法在私人临床数据集上进行了测试，并分别获得0.31和0.87的HI和CI。实验结果比现有方法更好，表明该方法产生的剂量分布接近诊所中批准的剂量。这些代码可在https://github.com/hired-ld/fa-net上找到。
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.