基于非局域注意力和多任务学习的胸部X线片肺实质分割方法

Non-local attention and multi-task learning based lung segmentation in chest X-ray

肺实质的准确分割是计算机辅助影像学诊断肺部疾病的关键。随着深度学习技术的发展,基于全卷积网络的图像分割模型取得了很好的效果,但对于边缘模糊和肺实质密度不均匀的情形仍会误分割。针对该问题,本文提出一种基于非局域注意力机制和多任务学习的胸部X线片图像肺实质分割方法。首先,基于残差连接的编-解码卷积网络提取肺实质多层级语义特征信息并预测肺实质边界轮廓;其次,通过非局域注意力机制建立肺实质轮廓与全局语义特征信息之间的相关性并增强轮廓区域特征信息权重;再次,基于增强的特征信息进行多任务监督学习,实现肺实质的准确分割;最后,在JSRT和Montgomery公开数据集上验证了本文方法的有效性和模型泛化能力,对比其他几种代表性的分割模型,其Dice系数和准确性最大分别提高1.99%和2.27%。实验结果表明,通过增强特征信息中边界轮廓的注意力,能有效减少肺实质密度不均匀时的误分割并提高模糊边缘的分割精度。

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system.With the development of deep learning,fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency.To solve this problem,this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning.Firstly,an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung.Secondly,a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region.Thirdly,a multi-task learning to predict lung field based on the enriched feature was conducted.Finally,experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset.The maximum improvement of Dice coefficient and accuracy were 1.99%and 2.27%,respectively,comparing with other representative algorithms.Results show that by enhancing the attention of boundary,this algorithm can improve the accuracy and reduce false segmentation.