基于主动学习的肺结节计算机辅助诊断交互审查技术

A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning

基于电子计算机断层扫描(CT)影像的肺结节自动检测可以有效辅助肺癌诊治,但当前缺乏有效的交互工具将放射科医生的判读结果实时记录并反馈,以优化后台算法模型。本文设计并研发了一个支持CT图像肺结节辅助诊断的在线交互审查系统,通过预置模型检测出肺结节展示给医生,医生利用专业知识对检测的肺结节进行标注,然后根据标注结果采用主动学习策略对内置模型进行迭代优化,以持续提高模型的准确性。本文以开源肺结节数据集——肺结节分析2016(LUNA16)的5~9号子集进行迭代实验,随着迭代次数的增加,模型的准确率、调和分数和交并比指标稳定提升,准确率从0.2139提高至0.5656。本文研究结果表明,该系统能在使用深度分割模型辅助医生诊断的同时,最大程度地利用医生的反馈信息来优化模型,迭代提高模型的准确性,从而更好地辅助医生工作。

Automatic detection of pulmonary nodule based on computer tomography(CT)images can significantly improve the diagnosis and treatment of lung cancer.However,there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization.This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images.Lung nodules were detected by the preset model and presented to doctors,who marked or corrected the lung nodules detected by the system with their professional knowledge,and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model.The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16)was used for iteration experiments.The precision,F1-score and MioU indexes were steadily improved with the increase of the number of iterations,and the precision increased from 0.2139 to 0.5656.The results in this paper show that the system not only uses deep segmentation model to assist radiologists,but also optimizes the model by using radiologists'feedback information to the maximum extent,iteratively improving the accuracy of the model and better assisting radiologists.