深度学习结合影像组学的肝脏肿瘤CT分割  

Accurate segmentation method of liver tumor CT based on the combination of deep learning and radiomics

作　　者：刘云鹏[1] 刘光品 王仁芳[1] 金冉[1] 孙德超[1] 邱虹 董晨[1] 李瑾[1] 洪国斌[2] Liu Yunpeng;Liu Guangpin;Wang Renfang;Jin Ran;Sun Dechao;Qiu Hong;Dong Chen;Li Jin;Hong Guobin(Zhejiang Wanli University,Ningbo 315000,China;The Fifth Hospital Affiliated to Zhongshan University,Zhongshan 528400,China)

机构地区：[1]浙江万里学院,宁波315000 [2]中山大学附属第五医院,中山528400

出　　处：《中国图象图形学报》2020年第10期2128-2141,共14页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61906170);教育部人文社科项目青年基金项目(17YJCZH076);浙江省自然科学基金项目(LY17F020001);浙江省教育厅一般项目(Y201840695);浙江省科技计划项目(LGF19F020008,LGF18F020001);宁波市科技计划项目(2019C50008);宁波市自然基金项目(2018A610156,2018A610164)。

摘　　要：目的从医学影像中进行肝脏与肿瘤分割是计算机辅助诊断和治疗的重要前提。常见的胸部和腹部扫描成像效果中,图像对比度偏低,边界模糊,需要医生丰富的临床解剖学知识才能准确地分割,所以精确的自动分割是一个极大的挑战。本文结合深度学习与医学影像组学,提出一种肝脏肿瘤CT(computed tomography)分割方法。方法首先建立一个级联的2D图像端到端分割模型对肝脏和肿瘤同时进行分割,分割模型采用U-Net深度网络框架,在编码器与解码器内部模块以及编码器与解码器层次之间进行密集连接,这种多样化的特征融合可以获取更准确的全局位置特征和更丰富的局部细节纹理特征;同时融入子像素卷积与注意力机制,有利于分割出更加微小的肿瘤区域;接着生成两个用于后处理的学习模型,一个基于影像组学的分类模型用于假阳性肿瘤的去除;另一个基于3D体素块的分类模型用于分割边缘的细化。结果实验数据来自某医院影像科300个肝癌病例CT,每个序列中的肝脏与肿瘤都是由10年以上的医学专家进行分割标注。对数据进行5倍交叉验证,敏感度(sensitivity)、命中率(positive predicted value)和戴斯系数(Dice coefficient)在验证结果中的平均值分别达到0.87±0.03、0.91±0.03和0.86±0.05,相比于性能第2的模型分别提高了0.03、0.02和0.04。结论肝脏肿瘤CT的精确分割可以形成有价值的术前预判、术中监测和术后评价,有助于制定完善的手术治疗方案,提高肝脏肿瘤手术的成功率,且该方法不局限于肝脏肿瘤的分割,同样也适用于其他医学影像组织器官与肿瘤的分割。Objective In clinical diagnosis,the manual segmentation of liver tumor needs to have anatomical knowledge and experience,which is highly subjective.Automatic segmentation of liver tumor based on computed tomography(CT)medical image research is important in guiding clinical diagnosis and treatment.It can form accurate preoperative prediction,intraoperative monitoring,and postoperative evaluation,which develop a perfect surgical treatment plan and improve the success rate of liver tumor surgery.However,a typical medical image is complex,which may contain many organs and tissues.In the imaging process,it often produces more or less interference information due to the influence of imaging equipment and human factors.According to the imaging principle and characteristics of medical imaging,not all information may be helpful for medical diagnosis.Whether CT or magnetic resonance imaging(MRI),as the common chest and abdomen scanning,in the imaging effect,the image contrast is low;the boundary is fuzzy;most of the time,accurate segmentation of interested organs and corresponding tumors requires clinical anatomy knowledge of doctors.In general,many boundaries are achieved through consultation between medical personnel.Notably,the automatic and accurate segmentation of liver tumor in medical images is a great challenge.In this paper,an effective segmentation method is proposed by combining multiple deep learning techniques with radiomics.Method First,a cascaded 2 D image end-to-end segmentation model is established to segment the liver and tumor simultaneously.The segmentation model adopts the U-Net deep network framework,which connects the internal modules of the encoder and decoder and the layers of the encoder and decoder intensively.This diversified feature fusion can obtain accurate global position features and abundant local details or physical characteristics.Sub-pixel convolution is added during upsampling by pixel shuffling multiple low-resolution feature images to output the high-resolution image and make up for

关 键 词：深度学习 影像组学 全卷积网络 注意力模型 密集连接 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]