肺CT图像的血管骨架化方法

在图像几何分析以及其他领域中,骨架化方法应用非常广泛。利用骨架表示图像可以保留图中的拓扑结构,减少冗余信息。在临床实践和治疗肺部疾病的过程中,如何有效地描绘并分析肺部血管骨架结构,对于计算机辅助诊断与辅助手术治疗是非常重要的。提出一种肺CT图像的血管骨架化方法:首先使用区域生长法将肺中的血管从胸腔其他组织中分离出来,接着使用数学形态学操作对分割结果进行处理,最后使用三维细化算法对血管进行骨架化。实验结果表明,该方法能快速、有效地实现肺部血管的骨架提取。

基于LSTM-ResNet模型的肺部肿瘤图像分割算法研究

肺癌是一种高发病率和高死亡率的恶性肿瘤,肺部CT图像的精准分割是肺癌患者病情诊断和治疗的有效途径。针对肺部肿瘤CT图像分割中存在肺肿瘤与周围组织对比度低、肺肿瘤边缘模糊和形状、大小及位置各不相同等问题,提出了一种基于LSTM和ResNet的组合模型LSTM-ResNet用于从肺部CT图像中准确地检测和分割肺肿瘤。实验结果表明本文方法能够精准分割出肺部CT图像中的肺肿瘤,满足临床诊断治疗和病理学分析研究的要求。

一种基于参数自适应的CT图像肺气道树分割方法

肺CT图像气道树分割对于肺呼吸功能测定和疾病诊断具有重要意义,但图像噪声和部分容积效应的影响会造成气管分割的泄漏,难于分割出微小的气管。本文利用肺气道树的解剖结构信息,对肺气道树进行分段处理,并提出一种分割参数自适应的方法,动态调整各气管段的分割参数。实验表明,该方法能提高分割的速度和精度,并有效防止泄漏。

基于高效通道注意力的UNet肺结节CT图像分割

肺癌是全球死亡率最高的癌症之一,肺结节作为肺癌早期诊断的重要依据,对其进行精准分割格外重要。为了帮助医生诊断肺部病变,本文提出一种改进的UNet肺结节分割方法。首先,在特征提取部分引入高效通道注意力网络(efficient channel attention for deep convolutional neural networks,EcaNet),提高UNet分割效果,使其具有良好的泛化能力。接着,为了降低模型参数量、提升算法分割性能,提出一种基于深度可分离卷积的特征融合模型,用深度可分离卷积代替传统卷积完成特征融合。然后,针对肺结节图像特点,将基于重叠度损失函数(dice loss)与加权交叉熵(weighted cross entropy,WCE)结合作为新的损失函数。最后,为验证所提算法Eca-UNet的有效性,在LIDC-IDRI肺结节公开数据集上进行评估。结果表明:Eca-UNet算法在DICE相似系数、MIOU上比UNet分割算法分别提高10.47、7.34个百分点;同时在训练速度上提升了10.10%,预测速度提升了11.56%。

Study of the impact of CT/CT image fusion radiotherapy on V_(20) and radiation pneumonitis of non-small cell lung cancer

Objective:The aim of our study was to investigate the value of CT/CT image fusion radiation treatment planning in non-small cell lung cancer(NSCLC) and the impact on V20 and radiation pneumonitis(RP).Methods:Patients who were pathologically or cytologically diagnosed of stage IIIA and IIIB NSCLC were treated with three-dimensional conformal radiation therapy(4000 cGy).Forty patients got at least 25% tumor reduction were randomly divided into two groups:group A of regular shrink field radiotherapy(20 cases) and group B of CT/CT image fused shrink field radiotherapy(20 cases).Dosage reached 6600 cGy.Clinical data,V20 and RP were observed within 3 months after radiotherapy.Statistical analysis was conducted for the NSCLC patients.Results:22.5%(9/40) patients got RP during follow-up.Group A accounted for 6 cases(30%),and group B had 3 cases(15%).There was no marked difference between the two groups(P = 0.256),univariate analysis revealed that the IV20 of A and B groups,and IV20 and CV20 of all patients were statistically related to the incidence of RP(P < 0.05).With Wilcoxon method assay,the ipsilateral lung V20 and contralateral lung V20 had statistical significance between the two groups(P < 0.05).Conclusion:The CT/CT image infusion treatment planning could increase the radical dosage with better tumor control probability but won't increase adverse reaction.

CT图像肺及肺病变区域分割方法综述

计算机断层扫描(computed tomography,CT)技术能为新冠肺炎(corona virus disease 2019,COVID-19)和肺癌等肺部疾病的诊断与治疗提供更全面的信息,但是由于肺部疾病的类型多样且复杂,使得对肺CT图像进行高质量的肺病变区域分割成为计算机辅助诊断的重难点问题。为了对肺CT图像的肺及肺病变区域分割方法的现状进行全面研究,本文综述了近年国内外发表的相关文献:对基于区域和活动轮廓的肺CT图像传统分割方法的优缺点进行比较与总结,传统的肺CT图像分割方法因其实现原理简单且分割速度快等优点,早期使用较多,但其存在分割精度不高的缺点,目前仍有不少基于传统方法的改进策略;重点分析了基于卷积神经网络(convolutional neural network,CNN)、全卷积网络(fully convolutional network,FCN)、U-Net和生成对抗网络(generative adversarial network,GAN)的肺CT图像分割网络结构改进模型的研究进展,基于深度学习的分割方法具有分割精度高、迁移学习能力强和鲁棒性高等优点,特别是在辅助诊断COVID-19病例时,基于深度学习方法的性能明显优于基于传统方法的性能;介绍肺及肺病变区域分割的常用数据集和评价指标,在解决如COVID-19数据样本量少等问题时,使用GAN以合成高质量的对抗性图像用以扩充数据集,从而增加训练样本的数量和多样性;讨论了肺CT图像的肺及肺病变区域的高精度分割策略的研究趋势、现有挑战和未来的研究方向。

CT image fusion in the evaluation of radiation treatment planning for non-small cell lung cancer

Objective: We studied the application of CT image fusion in the evaluation of radiation treatment planning for non-small cell lung cancer (NSCLC). Methods: Eleven patients with NSCLC, who were treated with three-dimensional con-formal radiation therapy, were studied. Each patient underwent twice sequential planning CT scan, i.e., at pre-treatment, and at mid-treatment for field reduction planning. Three treatment plans were established in each patient: treatment plan A was based on the pre-treatment planning CT scans for the first course of treatment, plan B on the mid-treatment planning CT scans for the second course of treatment, and treatment plan F on the fused images for the whole treatment. The irradiation doses received by organs at risk in the whole treatment with treatment A and B plans were estimated by the plus of the parameters in treatment plan A and B, assuming that the parameters involve the different tissues (i.e. V20=AV20+BV20), or the same tissues within an organ (i.e. Dmax=ADmax+BDmax). The assessment parameters in the treatment plan F were calculated on the basis of the DVH of the whole treatment. Then the above assessment results were compared. Results: There were marked differ-ences between the assessment results derived from the plus of assessment parameters in treatment plan A and B, and the ones derived from treatment plan F. Conclusion: When a treatment plan is altered during the course of radiation treatment, image fusion technique should be performed in the establishment of a new one. The estimation of the assessment parameters for the whole treatment with treatment plan A and B by simple plus, is inaccurate.