CN109461139A - 一种基于动态mri信息融合的肝癌定量分析方法 - Google Patents
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Abstract
本发明公开了一种基于动态MRI信息融合的肝癌定量分析方法,该方法结合机器学习方法,利用XGboost模型算法,进行自学习,自优化,构建活性肝癌细胞识别最优模型,优化了活性肝癌区域的检测手段,为医生对肝癌的TACE治疗疗效进行评估提供更为精准的临床信息,从而为肝癌的精准治疗方案的制定提供技术支持和可靠依据。
Description
技术领域
本发明属于医学图像处理领域,具体涉及到一种基于动态MRI信息融合的肝癌定量分析方法。
背景技术
随着医学影像学和计算机科学的不断发展与结合,利用计算机技术对临床医学数据进行分析是一种高准确率和高效率的辅助诊断方式,大大提高了疾病的预防率和治疗成功率。原发性肝癌是我国的常见病和多发病,占全世界肝癌疾病的比例很大,且肝癌早期诊断比较困难,多数情况下,患者在确诊之后,可治愈性治疗特征已经消失,因此针对原发性肝癌,现代临床医学采用经导管动脉化疗栓塞(TACE)的方式为患者进行治疗。基于此治疗方式的方法有很多,在临床应用方面,核磁共振成像(MRI)较CT在后续治疗评估中具有更高的应用价值。MRI可以排除TACE术中所用栓塞剂(碘油)的干扰。碘油在T2加权成像、动态增强等成像序列上表现均为明显低信号,而残留、复发灶表现为高信号,据此可明确区分肿瘤组织和相邻的栓塞剂。另外,通过设计不同的扫描序列可获得特定MRI信号,较CT可提供目标区域更多的信息。原发性肝癌在TACE术后,坏死区表现为肿瘤凝固性坏死,而坏死区域在MRI中的特异性非常高。因此,MRI可以为TACE疗效的评估提供更多的信息。另外,三维动态增强序列在动脉强化期呈现高信号,静脉期及平衡期呈现低信号。该序列既能完成与CT相同的快速动态增强扫描,又具有明显高于CT的组织分辨力,其病灶检出率与准确率均明显高于CT增强扫描,也优于其它MRI成像序列。
目前,在肝癌的TACE治疗后的疗效评估过程中,医生只能根据组织在动态增强MRI(DCMRI)的各项图像中的情况判断TACE的治疗效果。这种评估过程比较粗放,医生的工作量大,评估效率偏低。针对这种现状,本发明采用机器学习的方法对各期DCMRI图像进行三维融合,构建活性肝肿瘤的信息,以方便医生对TACE治疗肝癌的效果进行评估,提高医生对肝癌的诊断效率和诊断准确率,从而提高肝癌的治疗效果。
发明内容
本发明的目的是克服现有技术的不足,提供一种基于动态MRI信息融合的肝癌定量分析方法,该方法可以帮助医生做出准确的病情判断。
实现本发明目的的技术方案路径是:
一种基于动态MRI信息融合的肝癌定量分析方法,具体包括如下步骤:
1)对采集到的肝癌患者的DCMRI图像,采用概率图谱与形状模型相结合的方法,同时引入适当的肝脏图像的特征,构造目标函数,实现对肝脏DCMRI图像的准确而且快速的分割,从DCMRI图像中分割得到肝脏图像区域;
2)提取肝脏图像区域内各体素在DCMRI图像中的信号变化特征信息,作为活性肿瘤的识别特征;
3)利用XGBoost模型算法构建活性肝癌区域识别模型,将步骤2)中提取的各体素的DCMRI特征信息输入该识别模型中进行识别;
4)用训练集数据来对步骤3)构建的模型进行训练,并采用测试集数据对模型进行测试,获得模型的最优参数;
5)利用训练好的识别模型对TACE治疗肝癌的疗效进行评估,得到肝脏活性肿瘤区域,并进行定量分析和三维显示,为制定肝癌进一步治疗方案以及预后预测提供精确可靠的信息。
步骤3)中,所述的XGBoost模型算法,是建立一种树状模型,依靠样本数据的输入,进行自学习,自优化,得到模型最优参数。
步骤3)中,所述的识别,识别输出结果采用0和1表示该体素区域是否为活性肿瘤细胞区域,其中设置输出1为活性肿瘤区域,输出0为非活性肿瘤区域。
与现有的肝癌定量分析方法进行比较,本发明具有以下优点:
本发明提供的一种基于动态MRI信息融合的肝癌定量分析方法,在临床应用方面,DCMRI在TACE治疗方法中更具有价值。结合机器学习方法,利用XGboost算法,进行自学习,自优化,构建活性肝癌细胞识别最优模型,优化了活性肝癌区域的检测手段,为医生对肝癌的TACE治疗疗效进行评估提供更为精准的临床信息,从而为肝癌的精准治疗方案的制定提供技术支持和可靠依据。
说明书附图
图1为本发明的一种基于动态MRI信息融合的肝癌定量分析方法的流程图。
图2为样本训练的模拟图。
具体实施方式
下面结合附图和实施例对本发明做进一步阐述,但不是对本发明的限定。
实施例:
一种基于动态MRI信息融合的肝癌定量分析方法,具体包括如下步骤,如图1所示:
步骤1:采集7期肝脏DCMRI图像,其中包括注射药物之前的平扫期图像和注射药物之后肝动脉期、门静脉期和肝实质期的图像。
步骤2:用图像之间的互相关,互信息等方法将采集到的7期DCMRI图像进行层间对齐处理。
步骤3:针对层间对齐处理以后的肝脏DCMRI图像,采用概率图谱与形状模型相结合的方法,同时引入适当的肝脏图像的特征,构造目标函数,对肝脏DCMRI图像的准确而且快速的分割,从DCMRI图像中分割得到肝脏图像区域。
步骤4:提取肝脏图像上各体素内的DCMRI信号的变化信息特征,并将此变化信息作为判别是否是活性肿瘤区域的依据。
步骤5:将各体素内DCMRI信号的变化信息作为待输入的样本数据,并且将样本数据分为训练集和测试集两部分数据。
步骤6:如图2所示,构建采用XGBoost算法的活性肝癌区域识别模型,将各体素内DCMRI信号变化特征信息作为该模型的输入特征信息,将0和1作为该模型的输出,表示该体素区域是否为活性肿瘤细胞区域,,其中,设置0代表非活性肝癌肿瘤区域,1代表活性肝癌肿瘤区域。
步骤7:将训练集数据输入活性肝癌细胞识别模型,对模型进行训练。
步骤8:将测试集数据输入模型,对训练的模型进行测试,得到模型的最优参数。
步骤9:利用训练好的识别模型对TACE治疗肝癌的疗效进行评估,得到精确的肝脏活性肿瘤区域,并进行定量分析。
步骤10:根据模型得到的结果,把结果映射到平扫期DCMRI图像上,利用三维显示软件进行三维显示。
步骤11:根据对肝癌活性肿瘤区域的一系列分析结果,制定肝癌进一步治疗方案以及为预后预测提供精确可靠的信息。
上述步骤中的活性肝癌肿瘤区域识别模型,采用的是机器学习中XGboost模型算法,通过建立一种树状模型,依靠样本数据的输入,进行自学习,自优化,得到模型最优参数,将活性肿瘤和非活性肿瘤区域识别,上述模型的训练包括以下过程:
(1)将提取到的体素内DCMRI信号变化特征信息输入到XGboost模型;
(2)XGboost模型进行第一轮学习:寻找第一个分裂节点。找所有特征中的一个特征作为分裂节点进行分裂,并且计算损失值,再找一个特征作为分裂节点进行分裂,同时计算损失值,直到得到一个最小的损失值,按照最小损失值所对应的特征进行分裂,得到第一颗树状模型;
(3)XGboost模型进行第二轮学习:寻找第二个分裂节点。从剩下的特征中,寻找损失值最小的特征作为下一级分裂节点,分裂得到新的树状模型;
(4)按照上述方式,经过若干轮模型学习,在最优化进一步分裂的基础上,树状模型逐渐壮大,不断形成新的树状模型,同时得到损失值和节点分数;
(5)直到得到的XGboost模型的目标函数中的误差最小、节点分数达到最佳,则模型停止分裂,得到模型的最优参数。
Claims (3)
1.一种基于动态MRI信息融合的肝癌定量分析方法,其特征在于,具体包括如下步骤:
1)对采集到的肝癌患者的DCMRI图像,采用概率图谱与形状模型相结合的方法,同时引入适当的肝脏图像的特征,构造目标函数,实现对肝脏DCMRI图像的准确而且快速的分割,从DCMRI图像中分割得到肝脏图像区域;
2)提取肝脏图像区域内各体素在DCMRI图像中的信号变化特征信息,作为活性肿瘤的识别特征;
3)利用XGBoost模型算法构建活性肝癌区域识别模型,将步骤2)中提取的各体素的DCMRI特征信息输入该识别模型中进行识别;
4)用训练集数据来对步骤3)构建的模型进行训练,并采用测试集数据对模型进行测试,获得模型的最优参数;
5)利用训练好的识别模型对TACE治疗肝癌的疗效进行评估,得到肝脏活性肿瘤区域,并进行定量分析和三维显示,为制定肝癌进一步治疗方案以及预后预测提供精确可靠的信息。
2.根据权利要求1所述的一种基于动态MRI信息融合的肝癌定量分析方法,其特征在于,步骤3)中,所述的XGBoost模型算法,是建立一种树状模型,依靠样本数据的输入,进行自学习,自优化,得到模型最优参数。
3.根据权利要求1所述的一种基于动态MRI信息融合的肝癌定量分析方法,其特征在于,步骤3)中,所述的识别,识别输出结果采用0和1表示该体素区域是否为活性肿瘤细胞区域,其中设置输出1为活性肿瘤区域,输出0为非活性肿瘤区域。
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130223704A1 (en) * | 2012-02-28 | 2013-08-29 | Siemens Aktiengesellschaft | Method and System for Joint Multi-Organ Segmentation in Medical Image Data Using Local and Global Context |
US20140270447A1 (en) * | 2013-03-13 | 2014-09-18 | Emory University | Systems, methods and computer readable storage media storing instructions for automatically segmenting images of a region of interest |
CN104168923A (zh) * | 2012-03-05 | 2014-11-26 | 伯拉考成像股份公司 | 用于评价病理性组织内大分子转运的动态增强mri成像法和活性剂 |
CN104809723A (zh) * | 2015-04-13 | 2015-07-29 | 北京工业大学 | 基于超体素和图割算法的三维肝脏ct图像自动分割方法 |
CN105096310A (zh) * | 2014-05-06 | 2015-11-25 | 西门子公司 | 利用多通道特征在磁共振图像中分割肝脏的方法和系统 |
CN107784647A (zh) * | 2017-09-29 | 2018-03-09 | 华侨大学 | 基于多任务深度卷积网络的肝脏及其肿瘤分割方法及系统 |
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Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130223704A1 (en) * | 2012-02-28 | 2013-08-29 | Siemens Aktiengesellschaft | Method and System for Joint Multi-Organ Segmentation in Medical Image Data Using Local and Global Context |
CN104168923A (zh) * | 2012-03-05 | 2014-11-26 | 伯拉考成像股份公司 | 用于评价病理性组织内大分子转运的动态增强mri成像法和活性剂 |
US20140270447A1 (en) * | 2013-03-13 | 2014-09-18 | Emory University | Systems, methods and computer readable storage media storing instructions for automatically segmenting images of a region of interest |
CN105096310A (zh) * | 2014-05-06 | 2015-11-25 | 西门子公司 | 利用多通道特征在磁共振图像中分割肝脏的方法和系统 |
CN104809723A (zh) * | 2015-04-13 | 2015-07-29 | 北京工业大学 | 基于超体素和图割算法的三维肝脏ct图像自动分割方法 |
CN107784647A (zh) * | 2017-09-29 | 2018-03-09 | 华侨大学 | 基于多任务深度卷积网络的肝脏及其肿瘤分割方法及系统 |
Cited By (1)
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CN113782204A (zh) * | 2021-09-14 | 2021-12-10 | 深圳市南山区慢性病防治院 | 一种预测药物性肝损伤的方法、系统及存储介质 |
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