乳腺癌的多模态显微谱像分析及智能融合诊断研究  

Multimodal Microscopic Spectrum-Image Analysis and Intelligent Fusion Diagnosis of Breast Cancer

作　　者：吴青霞 李柏楠 惠紫阳 王子函 李运宏 尚林伟 尹建华[1] Wu Qingxia;Li Bainan;Hui Ziyang;Wang Zihan;Li Yunhong;Shang Linwei;Yin Jianhua(Department of Biomedical Engineering,College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China)

机构地区：[1]南京航空航天大学自动化学院生物医学工程系,江苏南京210016

出　　处：《中国激光》2024年第15期143-151,共9页Chinese Journal of Lasers

基　　金：国家自然科学基金(62375127,62105147);江苏省重点研发计划(BE2023812);南京航空航天大学前瞻布局专项基金(ILA-22022)。

摘　　要：显微成像以及荧光光谱技术是研究乳腺癌组织特性的重要手段,可有效反映组织形态结构和生化成分的变化。本课题组采用自行设计和加工的倒置荧光显微镜,对患者乳腺组织的显微图像和荧光光谱进行同步收集,然后进行明场图像和多波长荧光显微图像以及荧光光谱数据的综合分析和融合。在光谱分析方面,采用高斯拟合模型对荧光光谱进行分峰处理,以探究乳腺癌发展过程中荧光成分的变化规律。研究结果显示,乳腺癌组织在520 nm和635 nm中心波长处的荧光谱带峰面积与470 nm处的峰面积之比(A520/A470和A635/A470)约为正常乳腺组织的1.65倍和2.07倍。因此,本课题组提出将峰面积比A520/A470和A635/A470作为乳腺癌检测的潜在指标。此外,构建了基于显微图像和荧光光谱的谱像融合神经网络,并采用该网络实现了对乳腺癌的智能诊断,最终AUC(受试者工作特征曲线下面积)得分和测试集准确率分别达到了0.95和86.38%,明显优于各单模态模型。本研究结果表明,双模态显微成像和荧光光谱技术在乳腺癌分析、诊断中具有独特优势,结合深度学习构建的谱像融合网络可为乳腺癌智能诊断提供有效途径。Objective Breast cancer is among the most common malignant tumors and a serious threat to women’s health.Therefore,rapid and efficient screening for breast cancer is increasingly important.Currently,imaging and pathological examinations are the two main methods used for breast cancer diagnosis.Imaging examinations typically have shortcomings such as long examination time and radioactivity.Pathological examination,which is the gold standard for cancer diagnosis,has the disadvantages of complicated preparation and time-consuming processes.Therefore,a new intelligent method must be developed for breast cancer diagnosis to reduce the reliance on traditional techniques.Microscopic imaging and fluorescence spectroscopy are crucial tools for studying the characteristics of cancerous breast tissues and for effectively capturing changes in tissue morphology and biochemical composition.In this study,a self-designed and processed inverted fluorescence microscope was employed to simultaneously collect microscopic images and fluorescence spectra from breast tissues of patients.Methods First,the samples used in this study were obtained from patients with invasive breast cancer.The fresh tissue samples were cut into tissue blocks of approximately 3 mm×3 mm×2 mm size,quickly frozen within liquid nitrogen,and then cut into tissue sections with a thickness of 15μm.The entire process did not require staining of tissue sections.Next,microscopic images and spectra were collected.The collection equipment was designed and customized based on an inverted fluorescence microscope(IX51,Olympus).Bright field and fluorescence imaging modes of different wavelengths were achieved by switching excitation filters and dichroic mirrors and adjusting the light source.A total of 69 sets of multimodal microscopic images and 46 sets of spectral data(divided into purple and blue light excitation)were obtained from the tissue sections of 23 patients.The spectral data were preprocessed using baseline correction and third-order polynomial 30-point Sa

关 键 词：双模态显微成像 多波长荧光光谱 谱像融合 深度学习 智能诊断 

分 类 号：O436[机械工程—光学工程]