Review of Research on Biomedical Image Processing Based on Pattern Recognition
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摘要:
海量的生物医学图像蕴含着丰富的信息，模式识别算法能够从中挖掘规律并指导生物医学基础研究和临床应用。近年来，模式识别和机器学习理论和实践不断完善，尤其是深度学习的广泛研究和应用，促使人工智能、模式识别与生物医学的交叉研究成为了当前的前沿热点，相关的生物医学图像研究有了突破式的进展。该文首先简述模式识别的常用算法，然后总结了这些算法应用于荧光显微图像、组织病理图像、医疗影像等多种图像中的挑战性和国内外研究现状，最后对几个潜在研究方向进行了分析和展望。
Abstract:Pattern recognition algorithms can discover valuable information from mass data of biomedical images as guide for basic research and clinical application. In recent years, with improvement of the theory and practice of pattern recognition and machine learning, especially the appearance and application of deep learning, the crossing researches among artificial intelligence, pattern recognition, and biomedicine become a hotspot, and achieve many breakthrough successes in related fields. This review introduces briefly the common framework and algorithms of image pattern recognition, summarizes the applications of these algorithms to biomedical image analysis including fluorescence microscopic images, histopathological images, and medical radiological images, and finally analyzes and prospect several potential research directions.
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Key words:
- Image processing /
- Biomedical images /
- Pattern recognition /
- Deep learning
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表 1 常用的生物图像数据集
类型 数据集 数据量 特点 荧光显微图像 CYCLoPs[23] 超3×105幅蛋白荧光图像 标注酵母细胞中蛋白质16类亚细胞位置及表达量 HPA IF[24] 2.2×105幅IF图像 20余个细胞系的蛋白图像，标注34类亚细胞位置 2D HeLa[25] 862幅荧光显微图像 HeLa宫颈癌细胞系，标注10个标志蛋白的表达模式 2D CHO[26] 327幅荧光显微图像 中国仓鼠卵巢细胞图像，标注5个标志蛋白的表达模式 组织病理图像 BreakHis[27] 7909幅H&E图像 乳腺良性和恶性肿瘤图像，共8类病理状态 TCGA[28] 18462幅H&E图像 记录36类癌症的病理检查及治疗数据， TMAD[29] 3726幅IHC图像 对蛋白质着色的评分，分为4个等级 HPA IHC[30] 约106幅IHC图像 人体正常和癌症组织的蛋白图像，标注3类亚细胞位置 医疗影像图像 BRATS[31] 65幅MRI图像 经专家人工分割的脑胶质瘤患者的多对比度MR扫描图像，两组癌症分级 ADNI[32] 2000余名志愿者的MRI、PET图像 阿尔茨海默病患者和健康组对照 ISLES[33] 103位病人的MRI图像 缺血性中风病人图像，由专家人工分割出损伤的脑组织 DeepLesion[34] 32735幅CT图像 肾脏病变、骨病变、肺结节、淋巴结肿大等多种病理诊断 
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表 2 常用的生物图像处理工具
类型 处理工具 作用 通用 ImageJ[36] 对多种生物医学图像做如缩放、旋转、平滑、区域分割、像素统计等多种处理分析 CellProfiler[37] 分割荧光点或细胞，提取细胞的统计学特征 荧光显微图像 Squassh[38] 分割和定量亚细胞结构 DeepLoc[39] 基于荧光图像预测蛋白质的亚细胞位置 CellOrganizer[40] 对多种细胞亚结构建立生成式模型，产生新的细胞图像或视频 OMERO.searcher[41] 图像匹配和检索 组织病理图像 HistomicsML[42] 交互式机器学习系统，训练基于病理图像的分类器 IHC Profiler[43] IHC图像统计学特征提取，着色评分 iLocator[44-46] 基于IHC图像的蛋白质亚细胞位置预测系统 医疗影像图像 RayPlus[47] 在线的云端的智能医学影像平台，集成3维影像重建、专科影像分析等功能 Mimics[48] 一套高度整合而且易用的3D图像生成及编辑处理软件 ANTS[49] 提供了高级的工具用于大脑图像配准映射，在解释和可视化多维数据方面有优势 FSL[50] 用于分析fMRI，MRI和DTI大脑成像数据的综合软件库
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