CN112890766A - 一种乳腺癌辅助治疗设备 - Google Patents
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Abstract
本发明公开了一种乳腺癌辅助治疗设备,包括乳腺癌检测模块、定向辅助治疗模块,乳腺癌检测模块包括图像采集模块、预处理模块、生成器、判别器,图像采集模块用于采集乳腺钼靶摄片图像,并将图像数据发送至预处理模块,预处理模块对乳腺钼靶摄片图像进行预处理,将预处理后的图像发送至生成器,生成器对预处理后的图像采用卷积神经网络进行卷积,生成癌细胞和正常细胞的分割图像,判别器构建一个与生成器具有相同的卷积神经网络的网络结构,判别器与生成器构成生成对抗网络模型。本发明利用生成对抗模型进行乳腺癌图像分割,获取乳腺癌的病变特征图像,并针对图像的区域进行辅助治疗,整个治疗过程中减少了人工的干预,降低了治疗成本。
Description
技术领域
本发明涉及医疗技术领域,特别涉及一种乳腺癌辅助治疗设备。
背景技术
乳腺癌是发生于乳腺上皮或导管上皮的恶性肿瘤,病因尚不完全清楚,可能与家族史和乳腺癌相关基因、生殖因素、性激素、营养与饮食、环境因素等有关。早期乳腺癌多数无明显症状,多在健康普查中发现。大多为乳房无痛性肿块,晚期出现乳头回缩、乳腺皮肤“酒窝症”或橘皮样变、腋窝淋巴结肿大等表现。
近年来,乳腺癌的发病率在逐年上升,而乳腺癌的治疗手段却一直没有得到有效的改进。目前乳腺癌的诊疗流程是通过对病变位置的分析,得到乳腺癌的具体位置;再通过手术治疗、化学治疗、靶向治疗及放射治疗等多种治疗手段进行综合治疗。伴随乳腺癌逐年升高的发病率,传统的乳腺癌治疗方法明显增加了医护人员的工作负担,再加上目前临床医师数量严重短缺。因此,设计一种在临床工作中能够快速进行辅助诊断且具有很高的参考价值的乳腺癌辅助治疗设备显得尤为需要。
发明内容
为了至少解决或部分解决上述问题,提供一种乳腺癌辅助治疗设备,能够进行智能化诊断及治疗,可有效的为乳腺癌治疗提供参考价值,降低了治疗成本。
为了达到上述目的,本发明提供了如下的技术方案:
本发明一种乳腺癌辅助治疗设备,包括乳腺癌检测模块、定向辅助治疗模块,所述乳腺癌检测模块包括图像采集模块、预处理模块、生成器、判别器,所述图像采集模块用于采集乳腺钼靶摄片图像,并将图像数据发送至预处理模块,所述预处理模块对乳腺钼靶摄片图像进行预处理,将预处理后的图像发送至生成器,所述生成器对预处理后的图像采用卷积神经网络进行卷积,生成癌细胞和正常细胞的分割图像,所述判别器构建一个与生成器具有相同的卷积神经网络的网络结构,所述判别器与生成器构成生成对抗网络模型,自动调节生成器的参数,并通过所述生成器生成最终的癌细胞和正常细胞的分割图像,并将图像发送至定向辅助治疗模块,所述定向辅助治疗模块根据图像发送的癌细胞位置进行定向治疗。
作为本发明的一种优选技术方案,所述生成器的训练数据样本为xi为预处理后的乳腺钼靶摄片图像,yi为乳腺癌标签图;所述判别器将训练数据样本集为和生成器生成的图像集进行归类,若判别器将生成器生成的图像集与训练数据样本集为归为一类,那么调整判别器的调节参数,若判别器将生成器生成的图像集与训练数据样本集为分为两类,则调整生成器的调节参数,直到判别器与生成器没有更多的改进余地,输出生成器的最终图像。
作为本发明的一种优选技术方案,所述生成器输出的最终图像的计算公式为
LcGAN(G,D)=Ex,y~Pdata(x,y)[logD(x,y)]+Ex~Pdata(x),z~pz(z)[log1-D(x,G(x,z))]
LL1(G)=Ex,y~Pdata(x,y),z~pz(z)[||y-G(x,z))||1]
作为本发明的一种优选技术方案,所述生成器与判别器的卷积神经网络均采用卷积到批标准化到激活Relu函数的卷积单元形式。
作为本发明的一种优选技术方案,所述预处理模块的预处理包括对乳腺钼靶摄片图像依次进行灰度处理、归一化的处理步骤。
作为本发明的一种优选技术方案,所述定向治疗包括药物治疗、红外线治疗和放射治疗。
与现有技术相比,本发明的有益效果如下:
本发明利用生成对抗模型进行乳腺癌图像分割,获取乳腺癌的病变特征图像,并针对图像的区域进行辅助治疗,整个治疗过程中减少了人工的干预,降低了治疗成本,同时,分割的图像具有很大的参考价值,有利于乳腺癌的快速治疗。
附图说明
附图用来提供对本发明的进一步理解,并且构成说明书的一部分,与本发明的实施例一起用于解释本发明,并不构成对本发明的限制。在附图中:
图1是本发明的整体结构示意图;
图中:1、乳腺癌检测模块;2、定向辅助治疗模块;3、图像采集模块; 4、预处理模块;5、生成器;6、判别器。
具体实施方式
以下结合附图对本发明的优选实施例进行说明,应当理解,此处所描述的优选实施例仅用于说明和解释本发明,并不用于限定本发明。其中附图中相同的标号全部指的是相同的部件。
此外,如果已知技术的详细描述对于示出本发明的特征是不必要的,则将其省略。
实施例1
如图1所示,本发明提供一种乳腺癌辅助治疗设备,包括乳腺癌检测模块1、定向辅助治疗模块2,乳腺癌检测模块1包括图像采集模块3、预处理模块4、生成器5、判别器6,图像采集模块3用于采集乳腺钼靶摄片图像,并将图像数据发送至预处理模块4,预处理模块4对乳腺钼靶摄片图像进行预处理,将预处理后的图像发送至生成器5,生成器5对预处理后的图像采用卷积神经网络进行卷积,生成癌细胞和正常细胞的分割图像,判别器6构建一个与生成器5具有相同的卷积神经网络的网络结构,判别器6与生成器5构成生成对抗网络模型,自动调节生成器5的参数,并通过生成器5生成最终的癌细胞和正常细胞的分割图像,并将图像发送至定向辅助治疗模块2,定向辅助治疗模块2根据图像发送的癌细胞位置进行定向治疗。
生成器5的训练数据样本为xi为预处理后的乳腺钼靶摄片图像, yi为乳腺癌标签图;判别器6将训练数据样本集为和生成器5生成的图像集进行归类,若判别器6将生成器5生成的图像集与训练数据样本集为归为一类,那么调整判别器6的调节参数,若判别器6将生成器5 生成的图像集与训练数据样本集为分为两类,则调整生成器5的调节参数,直到判别器6与生成器5没有更多的改进余地,输出生成器5的最终图像。
生成器5输出的最终图像的计算公式为
LcGAN(G,D)=Ex,y~Pdata(x,y)[logD(x,y)]+Ex~Pdata(x),z~pz(z)[log1-D(x,G(x,z))]
LL1(G)=Ex,y~Pdata(x,y),z~pz(z)[||y-G(x,z)||1]
生成器5与判别器6的卷积神经网络均采用卷积到批标准化到激活Relu 函数的卷积单元形式。
预处理模块4的预处理包括对乳腺钼靶摄片图像依次进行灰度处理、归一化的处理步骤。
定向治疗包括药物治疗、红外线治疗和放射治疗。
本发明利用生成对抗模型进行乳腺癌图像分割,获取乳腺癌的病变特征图像,并针对图像的区域进行辅助治疗,整个治疗过程中减少了人工的干预,降低了治疗成本,同时,分割的图像具有很大的参考价值,有利于乳腺癌的快速治疗。
最后应说明的是:以上所述仅为本发明的优选实施例而已,并不用于限制本发明,尽管参照前述实施例对本发明进行了详细的说明,对于本领域的技术人员来说,其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。
Claims (6)
1.一种乳腺癌辅助治疗设备,其特征在于,包括乳腺癌检测模块(1)、定向辅助治疗模块(2),所述乳腺癌检测模块(1)包括图像采集模块(3)、预处理模块(4)、生成器(5)、判别器(6),所述图像采集模块(3)用于采集乳腺钼靶摄片图像,并将图像数据发送至预处理模块(4),所述预处理模块(4)对乳腺钼靶摄片图像进行预处理,将预处理后的图像发送至生成器(5),所述生成器(5)对预处理后的图像采用卷积神经网络进行卷积,生成癌细胞和正常细胞的分割图像,所述判别器(6)构建一个与生成器(5)具有相同的卷积神经网络的网络结构,所述判别器(6)与生成器(5)构成生成对抗网络模型,自动调节生成器(5)的参数,并通过所述生成器(5)生成最终的癌细胞和正常细胞的分割图像,并将图像发送至定向辅助治疗模块(2),所述定向辅助治疗模块(2)根据图像发送的癌细胞位置进行定向治疗。
4.根据权利要求1所述的一种乳腺癌辅助治疗设备,其特征在于,所述生成器(5)与判别器(6)的卷积神经网络均采用卷积到批标准化到激活Relu函数的卷积单元形式。
5.根据权利要求1所述的一种乳腺癌辅助治疗设备,其特征在于,所述预处理模块(4)的预处理包括对乳腺钼靶摄片图像依次进行灰度处理、归一化的处理步骤。
6.根据权利要求1所述的一种乳腺癌辅助治疗设备,其特征在于,所述定向治疗包括药物治疗、红外线治疗和放射治疗。
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